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1
Malikova, F. U., N. ZH Zhanat, A. K. Saginayeva, and R. S. Ryskeldy. "FEATURES OF FACIAL RECOGNITION." BULLETIN Series of Physics & Mathematical Sciences 69, no. 1 (March 10, 2020): 374–77. http://dx.doi.org/10.51889/2020-1.1728-7901.67.
Повний текст джерелаАнотація:
The facial recognition system is used to provide identification and authentication during functional testing. It can also be used to identify people in different situations. This article presents a comparative study of the algorithms used for facial isolation and recognition. Algorithms are general algorithms that match a recognizable face. The concept of each algorithm is explained and a corresponding description is given. In addition, the results of the algorithms are evaluated in a data set and are displayed as graphs for evaluating the effectiveness of each algorithm. Algorithms work with a common data set and display the percentage of functions obtained.
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Dirin, Amir, Nicolas Delbiaggio, and Janne Kauttonen. "Comparisons of Facial Recognition Algorithms Through a Case Study Application." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 14 (August 28, 2020): 121. http://dx.doi.org/10.3991/ijim.v14i14.14997.
Повний текст джерелаАнотація:
<p class="affiliations"><strong>Abstract— </strong>Computer visions and their applications have become important in contemporary life. Hence, researches on facial and object recognition have become increasingly important both from academicians and practitioners. Smart gadgets such as smartphones are nowadays capable of high processing power, memory capacity, along with high resolutions camera. Furthermore, the connectivity bandwidth and the speed of the interaction have significantly impacted the popularity of mobile object recognition applications. These developments in addition to computer vision’s algorithms advancement have transferred object’s recognitions from desktop environments to the mobile world. The aim of this paper to reveal the efficiency and accuracy of the existing open-source facial recognition algorithms in real-life settings. We use the following popular open-source algorithms for efficiency evaluations: Eigenfaces, Fisherfaces, Local Binary Pattern Histogram, the deep convolutional neural network algorithm, and OpenFace. The evaluations of the test cases indicate that among the compared facial recognition algorithms the OpenFace algorithm has the highest accuracy to identify faces. The findings of this study help the practitioner on their decision of the algorithm selections and the academician on how to improve the accuracy of the current algorithms even further.</p>
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Ahmad Khorsheed, Eman, and Zakiya Ali Nayef. "Face Recognition Algorithms: A Review." Academic Journal of Nawroz University 11, no. 3 (August 1, 2022): 202–7. http://dx.doi.org/10.25007/ajnu.v11n3a1432.
Повний текст джерелаАнотація:
Facial recognition is the method by which an individual's identity is determined by a facial image. With the support of this method, it is potential to use the face image of the person to document it in any safety system. Facial recognition methods for static images can approximately be classified into comprehensive approaches to comprehensive and feature-based approaches. The comprehensive systems use the whole raw face image as input, while feature-based approaches utilize limited face features and use their regular and decorative properties [1]. A huge number of facial recognition systems have been advanced in the past periods. In this paper, we review a varied range of approaches used to identify facial recognition, which contains LDA, PCA, SVM, and ICA.
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Popoola, J. A., and C. O. Yinka-Banjo. "Comparative analysis of selected facial recognition algorithms." Nigerian Journal of Technology 39, no. 3 (September 16, 2020): 896–904. http://dx.doi.org/10.4314/njt.v39i3.31.
Повний текст джерелаАнотація:
Systems and applications embedded with facial detection and recognition capabilities are founded on the notion that there are differences in face structures among individuals, and as such, we can perform face-matching using the facial symmetry. A widely used application of facial detection and recognition is in security. It is important that the images be processed correctly for computer-based facial recognition, hence, the usage of efficient, cost-effective algorithms and a robust database. This research work puts these measures into consideration and attempts to determine a cost-effective and reliable algorithm out of three algorithms examined.
Keywords: Haar-Cascade, PCA, Eigenfaces, Fisherfaces, LBPH, Face Recognition.
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BUKOWSKI, MICHAŁ. "REVIEW OF FACE RECOGNITION ALGORITHMS." PRZEGLĄD POLICYJNY 140, no. 4 (March 17, 2021): 209–43. http://dx.doi.org/10.5604/01.3001.0014.8469.
Повний текст джерелаАнотація:
Information technology of the 20th and 21st centuries “opened the way” to the automatic assessment of anthropometric facial features, facial gestures and other characteristic behaviours. Recognition is a very complex technical problem with a signifi cant practical effect. There are dedicated applications for this purpose. The article presents face recognition algorithms for 2D images, for three-dimensional spaces, and methods using neural networks. Linear and nonlinear, local and global, and hybrid methods of facial recognition are presented. The study understands the strengths and weaknesses of the laws governing the use of face recognition technology and, if possible, analyses their effi ciency. The methodological review has been created in connection with the idea of the author’s own fast algorithms and facial recognition.
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Zhu, Yan Li, Jun Chen, and Pei Xin Qu. "A Novel Discriminant Non-Negative Matrix Factorization and its Application to Facial Expression Recognition." Advanced Materials Research 143-144 (October 2010): 129–33. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.129.
Повний текст джерелаАнотація:
The paper proposes a novel discriminant non-negative matrix factorization algorithm and applies it to facial expression recognition. Unlike traditional non-negative matrix factorization algorithms, the algorithm adds discriminant constraints in low-dimensional weights. The experiments on facial expression recognition indicate that the algorithm enhances the discrimination capability of low-dimensional features and achieves better performance than other non-negative matrix factorization algorithms.
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Costa, Lucas José da, Thiago Luz de Sousa, Francisco Assis da Silva, Leandro Luiz de Almeida, Danillo Roberto Pereira, Almir Olivette Artero, and Marco Antonio Piteri. "ANÁLISE DE MÉTODOS DE DETECÇÃO E RECONHECIMENTO DE FACES UTILIZANDO VISÃO COMPUTACIONAL E ALGORITMOS DE APRENDIZADO DE MÁQUINA." COLLOQUIUM EXACTARUM 13, no. 2 (September 22, 2021): 01–11. http://dx.doi.org/10.5747/ce.2021.v13.n2.e354.
Повний текст джерелаАнотація:
The advancement in technology in recent decades has provided many facilities for humanity in various applications, and facial recognition technology is one of them. There are several problemsto be solved to perform face recognition from digital images, such as varying ambient lighting, changing the face physical characteristics and resolution of the images used. This work aimed to perform a comparative analysis between some of thedetection and facial recognition methods, as well as their execution time. We use the Eigenface, Fisherface and LBPH facial recognition algorithms in conjunction with the Haar Cascade facedetection algorithm, all from the OpenCV library. We also explored the use of CNN neural network for facial recognition in conjunction with the HOG facial detection algorithm, these from the Dlib library. The work aimed, besides analyzing the algorithms in relation to hit rates, factors such as reliability and execution time were also considered
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binghua, HE, CHEN zengzhao, LI gaoyang, JIANG lang, ZHANG zhao, and DENG chunlin. "An expression recognition algorithm based on convolution neural network and RGB-D Images." MATEC Web of Conferences 173 (2018): 03066. http://dx.doi.org/10.1051/matecconf/201817303066.
Повний текст джерелаАнотація:
Aiming at the problem of recognition effect is not stable when 2D facial expression recognition in the complex illumination and posture changes. A facial expression recognition algorithm based on RGB-D dynamic sequence analysis is proposed. The algorithm uses LBP features which are robust to illumination, and adds depth information to study the facial expression recognition. The algorithm firstly extracts 3D texture features of preprocessed RGB-D facial expression sequence, and then uses the CNN to train the dataset. At the same time, in order to verify the performance of the algorithm, a comprehensive facial expression library including 2D image, video and 3D depth information is constructed with the help of Intel RealSense technology. The experimental results show that the proposed algorithm has some advantages over other RGB-D facial expression recognition algorithms in training time and recognition rate, and has certain reference value for future research in facial expression recognition.
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a, Vinayak, and Rachana R. Babu. "Facial Emotion Recognition." YMER Digital 21, no. 05 (May 23, 2022): 1010–15. http://dx.doi.org/10.37896/ymer21.05/b5.
Повний текст джерелаАнотація:
Human expresses their mood and sometimes what they need through their expression. This project traces the mood of the human using a real time recognition system which will detect the emotion. It can be a smiling face, or it can be the face full of anger. Facial emotion recognition is one of the useful task and can be used as a base for many real-time applications. The example can be feedback through moods at any restaurants and hotels about their services and foods. It can be much impactful in the field of military. Its very usage can be helpful for recognizing the people’s behaviour at the border areas to find out the suspects between them. This project consists of various algorithms of machine as well as deep learning. Some of the libraries are: Keras, OpenCV, Matplotlib. Image processing is used in classifying the universal emotions like neutral, surprise, sad, angry, happy, disguist, fear. This project consists of two modules: (i)Processing and generating the model for the application using different algorithms and (ii) Application for using the model using OpenCV to recognize. A set of values obtained after processing those extracted features points are given as input to recognize the emotion. Keywords: facial emotion recognition, deep neural networks, automatic recognition database
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Kaur, Paramjit, Kewal Krishan, Suresh K. Sharma, and Tanuj Kanchan. "Facial-recognition algorithms: A literature review." Medicine, Science and the Law 60, no. 2 (January 21, 2020): 131–39. http://dx.doi.org/10.1177/0025802419893168.
Повний текст джерелаАнотація:
The face is an important part of the human body, distinguishing individuals in large groups of people. Thus, because of its universality and uniqueness, it has become the most widely used and accepted biometric method. The domain of face recognition has gained the attention of many scientists, and hence it has become a standard benchmark in the area of human recognition. It has turned out to be the most deeply studied area in computer vision for more than four decades. It has a wide array of applications, including security monitoring, automated surveillance systems, victim and missing-person identification and so on. This review presents the broad range of methods used for face recognition and attempts to discuss their advantages and disadvantages. Initially, we present the basics of face-recognition technology, its standard workflow, background and problems, and the potential applications. Then, face-recognition methods with their advantages and limitations are discussed. The concluding section presents the possibilities and future implications for further advancing the field.
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Coe, James, and Mustafa Atay. "Evaluating Impact of Race in Facial Recognition across Machine Learning and Deep Learning Algorithms." Computers 10, no. 9 (September 10, 2021): 113. http://dx.doi.org/10.3390/computers10090113.
Повний текст джерелаАнотація:
The research aims to evaluate the impact of race in facial recognition across two types of algorithms. We give a general insight into facial recognition and discuss four problems related to facial recognition. We review our system design, development, and architectures and give an in-depth evaluation plan for each type of algorithm, dataset, and a look into the software and its architecture. We thoroughly explain the results and findings of our experimentation and provide analysis for the machine learning algorithms and deep learning algorithms. Concluding the investigation, we compare the results of two kinds of algorithms and compare their accuracy, metrics, miss rates, and performances to observe which algorithms mitigate racial bias the most. We evaluate racial bias across five machine learning algorithms and three deep learning algorithms using racially imbalanced and balanced datasets. We evaluate and compare the accuracy and miss rates between all tested algorithms and report that SVC is the superior machine learning algorithm and VGG16 is the best deep learning algorithm based on our experimental study. Our findings conclude the algorithm that mitigates the bias the most is VGG16, and all our deep learning algorithms outperformed their machine learning counterparts.
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Raju, Chandrakala G., Rahul S. Hangal, Shashidhara A. R, and Srinatha T. D. "Identical Twins Facial Recognition System Using Cloud." International Journal of Engineering and Computer Science 9, no. 06 (June 24, 2020): 25070–74. http://dx.doi.org/10.18535/ijecs/v9i06.4500.
Повний текст джерелаАнотація:
Facial recognition algorithm should be able to work even when the similar looking people are found i.e. also in the extreme case of identical looking twins. An experimental data set which contains 40 images of 20 pairs of twins collected randomly from the internet. The training is done with the selected images of the twins using different training algorithms and inbuilt functions available. The extracted features are stored over the Amazon public cloud. As a part of testing phase random images from the dataset trained are selected and upon running it over the system we get the features of those images which then will be compared by extracting the features already stored in Amazon cloud. The stored values and the current image features are compared and result will be displayed on the GUI. Identical twin’s facial recognition system uses the machine learning, image processing algorithms and deep learning algorithms. Regardless of the conditions of the images acquired, distinguishing identical twins is significantly harder than distinguishing faces that are not identical twins for all the algorithms.
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Kamyab, Taraneh, Haitham Daealhaq, Ali Mojarrad Ghahfarokhi, Fatemehalsadat Beheshtinejad, and Ehsan Salajegheh. "Combination of Genetic Algorithm and Neural Network to Select Facial Features in Face Recognition Technique." International Journal of Robotics and Control Systems 3, no. 1 (January 5, 2023): 50–58. http://dx.doi.org/10.31763/ijrcs.v3i1.849.
Повний текст джерелаАнотація:
Face recognition methods are computational algorithms that follow aim to identify a person's image according to the bank of images they have of different people. So far, various methods have been proposed for face recognition, which can generally be divided into two categories based on face structure and based on facial features. Based on this, many algorithms have been introduced and used for face recognition. Genetic algorithm has been one of the successful algorithms for face recognition. In this article, we first briefly explained the genetic algorithm and then used the combination of neural network and genetic algorithm to select and classify facial features The presented method has been evaluated using individual features and combined features of the face region. Composite features perform better than face region features in experimental tests. Also, a comprehensive comparison with other facial recognition techniques available in the FERET database is included in this paper. The proposed method has produced a classification accuracy of 94%, which is a significant improvement and the best classification accuracy among the results established in other studies.
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Phillips, P. Jonathon, Amy N. Yates, Ying Hu, Carina A. Hahn, Eilidh Noyes, Kelsey Jackson, Jacqueline G. Cavazos, et al. "Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms." Proceedings of the National Academy of Sciences 115, no. 24 (May 29, 2018): 6171–76. http://dx.doi.org/10.1073/pnas.1721355115.
Повний текст джерелаАнотація:
Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.
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Gowda, Prof Shashank M., and Dr H. N. Suresh. "Facial Expression Recognition using Robust Algorithm based on Modern Machine Learning Technique." International Journal of Engineering and Advanced Technology 11, no. 5 (June 30, 2022): 30–39. http://dx.doi.org/10.35940/ijeat.e3535.0611522.
Повний текст джерелаАнотація:
Recently facial expression recognition has turned out to be an interesting field in research because of more demand for security and the advancement of mobile devices. Due to many serious incidents like terrorists’ attack, there arises more concern to develop the security systems mainly in certain places like airports and border crossings where identification and verification are mandatory. On the other hand, these surveillance systems aid to identify the missing person, even though it is based on robust facial expression recognition algorithms and on the developed database for facial expression recognition. However, the human faces are complex and multidimensional which make the facial gesture extraction to be very challenging. Obviously, in high secured applications facial expression recognition (FER) systems are mandatory to avoid incidents. In this paper, the automatic facial expression recognition system is developed based on the machine learning algorithms for classification. This research reveals the identification of FER for the ease of communication. Hybridization of Adaptive Kernel function based Extreme Learning Machine with Chicken Swarm Optimization (HAKELM-CSO) algorithm is introduced for identifying the accurate facial expression among the large database. In this work, an approach is developed by applying the machine learning techniques for the automated classification on the image region. The major purpose of this research work is to overcome the flaws of traditional algorithms and to improve the process of facial expression recognition which could be used in various applications.
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Dudekula, Usen, and Purnachand N. "Linear fusion approach to convolutional neural networks for facial emotion recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (March 1, 2022): 1489. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1489-1500.
Повний текст джерелаАнотація:
Facial expression recognition is a challenging problem in the scientific field of computer vision. Several face expression recognition (FER) algorithms are proposed in the field of machine learning, and deep learning to extract expression knowledge from facial representations. Even though numerous algorithms have been examined, several issues like lighting changes, rotations and occlusions. We present an efficient approach to enhance recognition accuracy in this study, advocates transfer learning to fine-tune the parameters of the pre-trained model (VGG19 model ) and non-pre-trained model convolutional neural networks (CNNs) for the task of image classification. The VGG19 network and convolutional network derive two channels of expression related characteristics from the facial grayscale images. The linear fusion algorithm calculates the class by taking an average of each classification decision on training samples of both channels. Final recognition is calculated using convolution neural network architecture followed by a softmax classifier. Seven basic facial emotions (BEs): happiness, surprise, anger, sadness, fear, disgust, and neutral facial expressions can be recognized by the proposed algorithm, The average accuracies for standard data set’s “CK+,” and “JAFFE,” 98.3 % and 92.4%, respectively. Using a deep network with one channel, the proposed algorithm can achieve well comparable performance.
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Tayfour Ahmed, Amira, Altahir Mohammed, and Moawia Yahia. "Performance comparisons of artificial neural network algorithms in facial expression recognition." International Journal of Engineering & Technology 4, no. 4 (September 13, 2015): 465. http://dx.doi.org/10.14419/ijet.v4i4.5069.
Повний текст джерелаАнотація:
This paper presents methods for identifying facial expressions. The objective of this paper is to present a combination of texture oriented method with dimensional reduction and use for training the Single-Layer Neural Network (SLN), Back Propagation Algorithm (BPA) and Cerebellar Model Articulation Controller (CMAC) for identifying facial expressions. The proposed methods are called intelligent methods that can accommodate for the variations in the facial expressions and hence prove to be better for untrained facial expressions. Conventional methods have limitations that facial expressions should follow some constraints. To achieve the expression detection accuracy, Gabor wavelet is used in different angles to extract possible textures of the facial expression. The higher dimensions of the extracted texture features are further reduced by using Fisher’s linear discriminant function for increasing the accuracy of the proposed method. Fisher’s linear discriminant function is used for transforming higher-dimensional feature vector into a two-dimensional vector for training proposed algorithms. Different facial emotions considered are angry, disgust, happy, sad, surprise and fear are used. The performance comparisons of the proposed algorithms are presented.
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Sai, B. Pavan Vishnu, Pallikonda Raj Sudarshan, Gunal C, and Rohit Kumar Gupta. "Choosing an Optimal Machine Learning Classifier for Facial Recognition." International Journal of Engineering Research in Computer Science and Engineering 9, no. 7 (July 21, 2022): 58–62. http://dx.doi.org/10.36647/ijercse/09.07.art013.
Повний текст джерелаАнотація:
Facial recognition is vital in today's technological world. There is a need to develop facial recognition systems which have high accuracy and at the same time high input to output response. To overcome the problem of low accuracy of facial recognition system we have attempted to combine different algorithm’s and develop a high accuracy facial recognition system. On combining different algorithms, we have observed that the Random Forest classifier achieved 96% accuracy in 2 secs. On the other hand, the Linear Discriminant Analysis classifier achieved 97% in 0 secs. Here we will compare the 6 classifiers to choose a optimal classifier.
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Paul, Sanmoy, and Sameer Kumar Acharya. "A comparative study on facial recognition algorithms." International Journal of Data Science and Big Data Analytics 1, no. 2 (May 5, 2021): 39. http://dx.doi.org/10.51483/ijdsbda.1.2.2021.39-50.
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Ou, Jun. "Classification Algorithms Research on Facial Expression Recognition." Physics Procedia 25 (2012): 1241–44. http://dx.doi.org/10.1016/j.phpro.2012.03.227.
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Hassan, S. M., A. Alghamdi, A. Hafeez, M. Hamdi, I. Hussain, and M. Alrizq. "An Effective Combination of Textures and Wavelet Features for Facial Expression Recognition." Engineering, Technology & Applied Science Research 11, no. 3 (June 2, 2021): 7172–76. http://dx.doi.org/10.48084/etasr.4080.
Повний текст джерелаАнотація:
In order to explore the accompanying examination goals for facial expression recognition, a proper combination of classification and adequate feature extraction is necessary. If inadequate features are used, even the best classifier could fail to achieve accurate recognition. In this paper, a new fusion technique for human facial expression recognition is used to accurately recognize human facial expressions. A combination of Discrete Wavelet Features (DWT), Local Binary Pattern (LBP), and Histogram of Gradients (HoG) feature extraction techniques was used to investigate six human emotions. K-Nearest Neighbors (KNN), Decision Tree (DT), Multi-Layer Perceptron (MLP), and Random Forest (RF) were chosen for classification. These algorithms were implemented and tested on the Static Facial Expression in Wild (SWEW) dataset which consists of facial expressions of high accuracy. The proposed algorithm exhibited 87% accuracy which is higher than the accuracy of the individual algorithms.
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Appati, Justice Kwame, Kofi Sarpong Adu-Manu, and Ebenezer Owusu. "Implementation of Missing Data Imputation Schemes in Face Recognition Algorithm under Partial Occlusion." Advances in Multimedia 2022 (June 15, 2022): 1–9. http://dx.doi.org/10.1155/2022/7374550.
Повний текст джерелаАнотація:
Face detection and recognition algorithms usually assume an image captured from a controlled environment. However, this is not always the case, especially in crowd control under surveillance or footage from a crime scene, where partial occlusions are unavoidable. Unfortunately, these occlusions have an adverse effect on the performance of these classical recognition algorithms. In this study, the performance of some selected data imputation schemes is evaluated on SVD/PCA frontal face recognition algorithm. The experiment was done on two datasets: Jaffe and MIT-CBCL, with immediate confirmation of the adverse effect of occlusion on the facial algorithm without implementing the imputation scheme. Further experimentation shows that IA is an ideal missing data imputation scheme that works best with the SVD/PCA facial recognition algorithm.
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Mehta, Dhwani, Mohammad Faridul Haque Siddiqui, and Ahmad Y. Javaid. "Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study." Sensors 19, no. 8 (April 21, 2019): 1897. http://dx.doi.org/10.3390/s19081897.
Повний текст джерелаАнотація:
Over the past two decades, automatic facial emotion recognition has received enormous attention. This is due to the increase in the need for behavioral biometric systems and human–machine interaction where the facial emotion recognition and the intensity of emotion play vital roles. The existing works usually do not encode the intensity of the observed facial emotion and even less involve modeling the multi-class facial behavior data jointly. Our work involves recognizing the emotion along with the respective intensities of those emotions. The algorithms used in this comparative study are Gabor filters, a Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) for feature extraction. For classification, we have used Support Vector Machine (SVM), Random Forest (RF), and Nearest Neighbor Algorithm (kNN). This attains emotion recognition and intensity estimation of each recognized emotion. This is a comparative study of classifiers used for facial emotion recognition along with the intensity estimation of those emotions for databases. The results verified that the comparative study could be further used in real-time behavioral facial emotion and intensity of emotion recognition.
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XiuJun, Zhang, and Liu Chang. "Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition." Scientific World Journal 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/608158.
Повний текст джерелаАнотація:
In order to overcome the limitation of traditional nonnegative factorization algorithms, the paper presents a generalized discriminant orthogonal non-negative tensor factorization algorithm. At first, the algorithm takes the orthogonal constraint into account to ensure the nonnegativity of the low-dimensional features. Furthermore, the discriminant constraint is imposed on low-dimensional weights to strengthen the discriminant capability of the low-dimensional features. The experiments on facial expression recognition have demonstrated that the algorithm is superior to other non-negative factorization algorithms.
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Onyema, Edeh Michael, Piyush Kumar Shukla, Surjeet Dalal, Mayuri Neeraj Mathur, Mohammed Zakariah, and Basant Tiwari. "Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet." Journal of Healthcare Engineering 2021 (December 6, 2021): 1–8. http://dx.doi.org/10.1155/2021/5196000.
Повний текст джерелаАнотація:
The use of machine learning algorithms for facial expression recognition and patient monitoring is a growing area of research interest. In this study, we present a technique for facial expression recognition based on deep learning algorithm: convolutional neural network (ConvNet). Data were collected from the FER2013 dataset that contains samples of seven universal facial expressions for training. The results show that the presented technique improves facial expression recognition accuracy without encoding several layers of CNN that lead to a computationally costly model. This study proffers solutions to the issues of high computational cost due to the implementation of facial expression recognition by providing a model close to the accuracy of the state-of-the-art model. The study concludes that deep l\earning-enabled facial expression recognition techniques enhance accuracy, better facial recognition, and interpretation of facial expressions and features that promote efficiency and prediction in the health sector.
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Tkachenko, Oleksandr, and Myroslav Boiko. "Some Aspects of Face Recognition: Models, Algorithms, Methods, Systems, Applications." Digital Platform: Information Technologies in Sociocultural Sphere 4, no. 1 (July 2, 2021): 79–95. http://dx.doi.org/10.31866/2617-796x.4.1.2021.236949.
Повний текст джерелаАнотація:
The purpose of the article is to research, analyze and consider the general problems and prospects of using existing approaches to face recognition (areas of application, features and differences). The research methodology consists of semantic analysis methods of the basic concepts in this subject area (theory and practice of pattern recognition, in particular, facial images). The article considers the existing approaches to the development of systems for face recognition. The novelty of the research is the solution of facial recognition problems to determine access rights and authentication. Conclusions. The existing problems analyzed and the prospects for using facial recognition algorithms are becoming more accurate. Facial recognition has become an important part of artificial intelligence because it is used in social media, digital cameras and smart home automation.
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Dijmărescu, Irina, Mariana Iatagan, Iulian Hurloiu, Marinela Geamănu, Ciprian Rusescu, and Adrian Dijmărescu. "Neuromanagement decision making in facial recognition biometric authentication as a mobile payment technology in retail, restaurant, and hotel business models." Oeconomia Copernicana 13, no. 1 (March 30, 2022): 225–50. http://dx.doi.org/10.24136/oc.2022.007.
Повний текст джерелаАнотація:
Research background: With growing evidence of biometric identification techniques as authentication, there is a pivotal need for comprehending contactless payments by use of facial recognition algorithms in retail, restaurant, and hotel business models. Purpose of the article: In this research, previous findings were cumulated showing that harnessing facial recognition payment applications as software-based contactless biometric algorithms results in remarkably qualitative enhancement in purchasing experience. Methods: Throughout March and November 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was carried out, with search terms including ?facial recognition payment technology?, ?facial recognition payment system?, ?facial recognition payment application?, ?face recognition-based payment service?, ?facial authentication for mobile payment transactions?, and ?contactless payment through facial recognition algorithms.? As the analyzed research was published between 2017 and 2021, only 187 articles satisfied the eligibility criteria. By removing questionable or unclear findings (limited/nonessential data), results unsubstantiated by replication, too general content, or having quite similar titles, 38, mainly empirical, sources were selected. The Systematic Review Data Repository was harnessed, a software program for the gathering, processing, and analysis of data for our systematic review. The quality of the selected scholarly sources was assessed by employing the Mixed Method Appraisal Tool. Findings & value added: Harnessing facial recognition payment applications as software-based contactless biometric algorithms results in remarkably qualitative enhancement in purchasing experience. Subsequent attention should be directed to whether perceived value and trust shape customers? adoption of biometric recognition payment devices.
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Šumak, Boštjan, Saša Brdnik, and Maja Pušnik. "Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study." Sensors 22, no. 1 (December 21, 2021): 20. http://dx.doi.org/10.3390/s22010020.
Повний текст джерелаАнотація:
To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.
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Pandey, Amit, Aman Gupta, and Radhey Shyam. "FACIAL EMOTION DETECTION AND RECOGNITION." International Journal of Engineering Applied Sciences and Technology 7, no. 1 (May 1, 2022): 176–79. http://dx.doi.org/10.33564/ijeast.2022.v07i01.027.
Повний текст джерелаАнотація:
Facial emotional expression is a part of face recognition, it has always been an easy task for humans, but achieving the same with a computer algorithm is challenging. With the recent and continuous advancements in computer vision and machine learning, it is possible to detect emotions in images, videos, etc. A face expression recognition method based on the Deep Neural Networks especially the convolutional neural network (CNN) and an image edge detection is proposed. The edge of each layer of the image is retrieved in the convolution process after the facial expression image is normalized. To maintain the texture picture's edge structure information, the retrieved edge information is placed on each feature image. In this research, several datasets are investigated and explored for training expression recognition models. The purpose of this paper is to make a study on face emotion detection and recognition via Machine learning algorithms and deep learning. This research work will present deeper insights into Face emotion detection and Recognition. It will also highlight the variables that have an impact on its efficacy.
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Fauziah, Fauziah. "The Determining Gender Using Facial Recognition Based On Neural Network With Backpropagation." Data Science: Journal of Computing and Applied Informatics 2, no. 1 (February 1, 2018): 53–61. http://dx.doi.org/10.32734/jocai.v2.i1-96.
Повний текст джерелаАнотація:
One area of science that can apply facial recognition applications is artificial intelligence. The algorithms used in facial recognition are quite numerous and varied, but they all have the same three basic stages, face detection, facial extraction and facial recognition (Face Recognition) . Facial recognition applications using artificial intelligence as a major component, especially artificial neural networks for processing and facial identification are still not widely encountered. Ba ckpropagation is a learning algorithm to minimize the error rate by adjusting the weights based on the desired output and target differences. The test results of 30 images have the average value of mse is 0.14796 and the best value of mse on the test of man number 3 with mse value 0.1488 and mean 0.0047 while for the female number 2 with mse value 0.1497 and niali mean 0.0047.
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Azar, Mitra. "Algorithmic Facial Image." A Peer-Reviewed Journal About 7, no. 1 (July 6, 2018): 26–35. http://dx.doi.org/10.7146/aprja.v7i1.115062.
Повний текст джерелаАнотація:
Facial tracking technologies have been incorporated in digital cameras for many years, and are offered to users of social networks such as Facebook to facilitate and automatize tagging (the process of recognizing one’s face in a picture and associating it with a user’s profile) and image sharing. Nevertheless, in recent times, facial recognition technologies seem to have taken a new turn, and from the simple recognition of faces with cameras and social networks they have become embedded in mainstream security technologies as much as in entertaining ‘face swap’ apps, transforming the social and cultural implications of the selfie. This paper examines the political implications of new technologies for facial recognition, and proposes a new type of selfie aesthetic characterized by new forms of human and machinic agency. The paper argues that when the selfie becomes mediated by new tracking technologies for security system and entertainment based on face-recognition algorithms, the selfie becomes an ‘Algorithmic Facial Image’.
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Suwarno, Suwarno, and Kevin Kevin. "Analysis of Face Recognition Algorithm: Dlib and OpenCV." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 4, no. 1 (July 20, 2020): 173–84. http://dx.doi.org/10.31289/jite.v4i1.3865.
Повний текст джерелаАнотація:
In face recognition there are two commonly used open-source libraries namely Dlib and OpenCV. Analysis of facial recognition algorithms is needed as reference for software developers who want to implement facial recognition features into an application program. From Dlib algorithm to be analyzed is CNN and HoG, from OpenCV algorithm is DNN and HAAR Cascades. These four algorithms are analyzed in terms of speed and accuracy. The same image dataset will be used to test, along with some actual images to get a more general analysis of how algorithm will appear in real life scenarios. The programming language used for face recognition algorithms is Python. The image dataset will come from LFW (Labeled Faces in the Wild), and AT&T, both of which are available and ready to be downloaded from the internet. Pictures of people around the UIB (Batam International University) is used for actual images dataset. HoG algorithm is fastest in speed test (0.011 seconds / image), but the accuracy rate is lower (FRR = 27.27%, FAR = 0%). DNN algorithm is the highest in level of accuracy (FRR = 11.69%, FAR = 2.6%) but the lowest speed (0.119 seconds / picture). There is no best algorithm, each algorithm has advantages and disadvantages.Keywords: Python, Face Recognition, Analysis, Speed, Accuracy.
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Abbas, Hawraa H., Bilal Z. Ahmed, and Ahmed Kamil Abbas. "3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms." Cybernetics and Information Technologies 19, no. 2 (June 1, 2019): 28–37. http://dx.doi.org/10.2478/cait-2019-0013.
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Abstract The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognition analysis is based on defining a set of coherent parts. Those parts can be considered as latent factors in the face shape space. Non-negative matrix Factorisation technique is used to segment the 3D faces to coherent regions. The best recognition performance is achieved when the vertices of 20 face regions are utilised as a feature vector for recognition task. The region-based 3D face recognition approach provides a 96.4% recognition rate in FRGCv2 dataset.
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Weng, Zhi, Shaoqing Liu, Zhiqiang Zheng, Yong Zhang, and Caili Gong. "Cattle Facial Matching Recognition Algorithm Based on Multi-View Feature Fusion." Electronics 12, no. 1 (December 29, 2022): 156. http://dx.doi.org/10.3390/electronics12010156.
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In the process of collecting facial images of cattle in the field, some features of the collected images end up going missing due to the changeable posture of the cattle, which makes the recognition accuracy decrease or impossible to recognize. This paper verifies the practical effects of the classical matching algorithms ORB, SURF, and SIFT in bull face matching recognition. The experimental results show that the traditional matching algorithms perform poorly in terms of matching accuracy and matching time. In this paper, a new matching recognition model is constructed. The model inputs the target cattle facial data from different angles into the feature extraction channel, combined with GMS (grid-based motion statistics) algorithm and random sampling consistent algorithm, to achieve accurate recognition of individual cattle, and the recognition process is simple and fast. The recognition accuracy of the model was 85.56% for the Holstein cow face dataset, 82.58% for the Simmental beef cattle, and 80.73% for the mixed Holstein and Simmental beef cattle dataset. The recognition model constructed in the study can achieve individual recognition of cattle in complex environments, has good robustness to matching data, and can effectively reduce the effects of data angle changes and partial features missing in cattle facial recognition.
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Wang, Qing Wei, and Zi Lu Ying. "Facial Expression Recognition Algorithm Based on Gabor Texture Features and Adaboost Feature Selection via Sparse Representation." Applied Mechanics and Materials 511-512 (February 2014): 433–36. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.433.
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This paper proposed a new facial expression recognition algorithm based on gabor texture features and Adaboost feature selection via SRC(sparse representation classification). Five scales and eight orientations of Gabor wavelet filters were used in this paper to extract gabor features. For an image of size , the number of gabor features is 163840, In order to extract the most effective features for FER(facial expression recognition), Adaboost algorithm is used for feature selection. This paper divided 7 facial expressions into two categories, where the neutral expression as the first class and the remaining six expressions as the second class. In each size and orientation 110 features are selected. At last 4400 features are selected combined SRC algorithm for FER. Test experiments were performed on Japanese female JAFFE facial expression database. Compared with the traditional expression recognition algorithms such as 2DPCA+SVM, LDA+SVM, the new algorithm achieved a better recognition rate, which shows the effectiveness of the proposed new algorithm.
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Liu, Chang, Kun He, Ji Liu Zhou, and Yan Li Zhu. "Facial Expression Recognition Based on Orthogonal Nonnegative CP Factorization." Advanced Materials Research 143-144 (October 2010): 111–15. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.111.
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Facial Expression recognition based on Non-negative Matrix Factorization (NMF) requires the object images should be vectorized. The vectorization leads to information loss, since local structure of the images is lost. Moreover, NMF can not guarantee the uniqueness of the decomposition. In order to remedy these limitations, the facial expression image was considered as a high-order tensor, and an Orthogonal Non-negative CP Factorization algorithm (ONNCP) was proposed. With the orthogonal constrain, the low-dimensional presentations of samples were non-negative in ONNCP. The convergence characteristic of the algorithm was proved. The experiments indicate that, compared with other non-negative factorization algorithms, the algorithm proposed in the paper reduces the redundancy of the base image and has better recognition rate in facial expression recognition.
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Salama AbdELminaam, Diaa, Abdulrhman M. Almansori, Mohamed Taha, and Elsayed Badr. "A deep facial recognition system using computational intelligent algorithms." PLOS ONE 15, no. 12 (December 3, 2020): e0242269. http://dx.doi.org/10.1371/journal.pone.0242269.
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The development of biometric applications, such as facial recognition (FR), has recently become important in smart cities. Many scientists and engineers around the world have focused on establishing increasingly robust and accurate algorithms and methods for these types of systems and their applications in everyday life. FR is developing technology with multiple real-time applications. The goal of this paper is to develop a complete FR system using transfer learning in fog computing and cloud computing. The developed system uses deep convolutional neural networks (DCNN) because of the dominant representation; there are some conditions including occlusions, expressions, illuminations, and pose, which can affect the deep FR performance. DCNN is used to extract relevant facial features. These features allow us to compare faces between them in an efficient way. The system can be trained to recognize a set of people and to learn via an online method, by integrating the new people it processes and improving its predictions on the ones it already has. The proposed recognition method was tested with different three standard machine learning algorithms (Decision Tree (DT), K Nearest Neighbor(KNN), Support Vector Machine (SVM)). The proposed system has been evaluated using three datasets of face images (SDUMLA-HMT, 113, and CASIA) via performance metrics of accuracy, precision, sensitivity, specificity, and time. The experimental results show that the proposed method achieves superiority over other algorithms according to all parameters. The suggested algorithm results in higher accuracy (99.06%), higher precision (99.12%), higher recall (99.07%), and higher specificity (99.10%) than the comparison algorithms.
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Yu, Guiping. "Emotion Monitoring for Preschool Children Based on Face Recognition and Emotion Recognition Algorithms." Complexity 2021 (March 2, 2021): 1–12. http://dx.doi.org/10.1155/2021/6654455.
Повний текст джерелаАнотація:
In this paper, we study the face recognition and emotion recognition algorithms to monitor the emotions of preschool children. For previous emotion recognition focusing on faces, we propose to obtain more comprehensive information from faces, gestures, and contexts. Using the deep learning approach, we design a more lightweight network structure to reduce the number of parameters and save computational resources. There are not only innovations in applications, but also algorithmic enhancements. And face annotation is performed on the dataset, while a hierarchical sampling method is designed to alleviate the data imbalance phenomenon that exists in the dataset. A new feature descriptor, called “oriented gradient histogram from three orthogonal planes,” is proposed to characterize facial appearance variations. A new efficient geometric feature is also proposed to capture facial contour variations, and the role of audio methods in emotion recognition is explored. Multifeature fusion can be used to optimally combine different features. The experimental results show that the method is very effective compared to other recent methods in dealing with facial expression recognition problems about videos in both laboratory-controlled environments and outdoor environments. The method performed experiments on expression detection in a facial expression database. The experimental results are compared with data from previous studies and demonstrate the effectiveness of the proposed new method.
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Dey, Sreya. "A Facial Recognition System." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3863–64. http://dx.doi.org/10.22214/ijraset.2022.45904.
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Abstract: This paper introduces the design, implementation, and validation of a Digital Signal Processor (DSP) -based Prototype face recognition and authentication system. This system is designed to capture image sequences, detect facial features in photos, and detect and verify a person. The current application uses images captured on a webcam and compares them to archived websites using the Comprehensive Component Analysis (PCA) and Discrete Cosine Transform (DCT) methods. Initially, realtime verification of the captured images was performed using a PC-based program with algorithms developed in MATLAB. Next, the TMS320C6713DSP-based prototype system is upgraded and validated in real-time. Several tests are performed on different sets of images, and the performance and speed of the proposed system are measured in real-time. Finally, the result confirmed that the proposed system could be used in a variety of applications that are not possible in standard PC-based applications. Also, better results were seen from DCT analysis than PCA results.
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Ming, Ye, Hu Qian, and Liu Guangyuan. "CNN-LSTM Facial Expression Recognition Method Fused with Two-Layer Attention Mechanism." Computational Intelligence and Neuroscience 2022 (October 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/7450637.
Повний текст джерелаАнотація:
When exploring facial expression recognition methods, it is found that existing algorithms make insufficient use of information about the key parts that express emotion. For this problem, on the basis of a convolutional neural network and long short-term memory (CNN-LSTM), we propose a facial expression recognition method that incorporates an attention mechanism (CNN-ALSTM). Compared with the general CNN-LSTM algorithm, it can mine the information of important regions more effectively. Furthermore, a CNN-LSTM facial expression recognition method incorporating a two-layer attention mechanism (ACNN-ALSTM) is proposed. We conducted comparative experiments on Fer2013 and processed CK + datasets with CNN-ALSTM, ACNN-ALSTM, patch based ACNN (pACNN), Facial expression recognition with attention net (FERAtt), and other networks. The results show that the proposed ACNN-ALSTM hybrid neural network model is superior to related work in expression recognition.
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Rossi, Rogério, Marcos Agenor Lazarini, and Kechi Hirama. "Systematic Literature Review on the Accuracy of Face Recognition Algorithms." EAI Endorsed Transactions on Internet of Things 8, no. 30 (September 12, 2022): e5. http://dx.doi.org/10.4108/eetiot.v8i30.2346.
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Real-time facial recognition systems have been increasingly used, making it relevant to address the accuracy of these systems given the credibility and trust they must offer. Therefore, this article seeks to identify the algorithms currently used by facial recognition systems through a Systematic Literature Review that considers recent scientific articles, published between 2018 and 2021. From the initial collection of ninety-three articles, a subset of thirteen was selected after applying the inclusion and exclusion procedures. One of the outstanding results of this research corresponds to the use of algorithms based on Artificial Neural Networks (ANN) considered in 21% of the solutions, highlighting the use of Convolutional Neural Network (CNN). Another relevant result is the identification of the use of the Viola-Jones algorithm, present in 19% of the solutions. In addition, from this research, two specific facial recognition solutions associated with access control were found considering the principles of the Internet of Things, one being applied to access control to environments and the other applied to smart cities.
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Liu, Chun Hui, Zhao Zheng, and Feng Gao. "Facial Expression Recognition Based on 2D Gabor Transforms and SVM." Applied Mechanics and Materials 58-60 (June 2011): 238–42. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.238.
Повний текст джерелаАнотація:
In the facial expression recognition, a dimension disaster will arise when taking the coefficient of Gabor transforms as the expression eigenvectors. To avoid this issue we draw grids on facial region, making the mean coefficient value of Gabor transforms of each gird as the eigenvectors. Furthermore we classify the expression by constructing the multi-class C-SVC, improved the accuracy and speed of the algorithm by dropping the redundant features using sequential backward selection. The experimental result proves the superiority of the algorithm we proposed to other algorithms.
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LU, HUCHUAN, DONG WANG, YEN-WEI CHEN, and HAO CHEN. "A NOVEL TEXTURE-BASED MULTI-LINEAR ANALYSIS ALGORITHM FOR FACE RECOGNITION." International Journal of Image and Graphics 11, no. 04 (October 2011): 495–508. http://dx.doi.org/10.1142/s021946781100424x.
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In this paper, a multi-linear approach based on texture features for face recognition is proposed. First, we extract fragment-based texture features of the facial images using the local binary pattern (LBP) descriptors, which capture both shape and texture information and also are robust to illumination variations. Second, we propose high-order orthogonal iteration (HOOI) algorithm that obtains optimum truncated factor-specific modes, which are not guaranteed in the standard N-mode SVD algorithm, in an iterative manner. Finally, we apply HOOI to obtain a compact and effective representation of the facial images based on the texture features. Our representation yields improved facial recognition rates relative to standard eigenface, tensorface, and other popular algorithms, especially when the facial images are confronted by a variety of viewpoints and illuminations. To evaluate the validity of our approach, a series of experiments are performed on the CMU-PIE facial databases.
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Mehta, Munish. "Facial Recognition in Public Areas." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 2, no. 2 (June 7, 2021): 1–7. http://dx.doi.org/10.54060/jieee/002.02.013.
Повний текст джерелаАнотація:
The security of information nowadays is very significant and difficult, so there are a number of ways to improve security. Especially in public areas like airports, railway stations, Universities, ATMs, etc. and security cameras are presently common in these areas. So, in this paper, we are presenting how Facial recognition can be used in public areas like airports, toll gates, offices, etc. We are comparing or matching a face of a person who we want to detect, with the video which is recorded through CCTV. There are certain algorithms to detect faces from video like through HAAR cascades, eigenface, fisher face, etc. open-source computer vision library is used for facial recognition.
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Kumar, Santosh, and Sanjay Kumar Singh. "Feature Selection and Recognition of Face by using Hybrid Chaotic PSO-BFO and Appearance-Based Recognition Algorithms." International Journal of Natural Computing Research 5, no. 3 (July 2015): 26–53. http://dx.doi.org/10.4018/ijncr.2015070102.
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Swarm intelligence based approaches are a recent optimization algorithm that simulates the groups (collective) behavior of decentralized and self-organized systems and have gained more proliferation due to a variety of applications and uses in the feature selection to solve the complex problems and classify the objects based on chosen optimal set of features. Feature selection is a process that selects a subset from the extracted features sets according to some criterions for optimization. In computer vision based face recognition systems, feature selection, and representation algorithms play an important role for the selection of optimal, and discriminatory sets of facial feature vectors from the face database. This paper presents a novel approach for facial feature selection by using Hybrid Particle Swarm Optimization (PSO), and Bacterial Foraging Optimization (BFO) optimization algorithms. The hybrid approach consists of two parts: (1) two types of chaotic mappings are introduced in different phase of proposed hybrid algorithms which preserve the huge diversity of population and improve the global searching and exploration capability; (2) In proposed hybrid approach, appearance based (holistic) face representation and recognition approaches such as Principal Component Analysis (PCA), Local Discriminant Analysis (LDA), Independent Component Analysis (ICA) and Discrete Cosine Transform (DCT) extract feature vectors from the Yale face database. Then features are selected by applying hybrid Chaotic PSO and BFO algorithms for the selection of optimal set of features; it quickly searches the feature subspace of facial features that is the most beneficial for classification and recognition of individuals. From the experimental results, the authors have compared the performance of proposed hybrid approach with existing approaches and conclude that hybrid approach can be efficiently used for feature selection for classification and recognition of face of individuals.
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Kusnadi, Adhi, Leondy ., Lianna Nathania, Ivransa Zuhdi Pane, Marlinda Vasty Overbeek, and Syarief Gerald Prasetya. "Image Processing for Improvement of Facial Keypoints Detector." Webology 19, no. 1 (January 20, 2022): 676–91. http://dx.doi.org/10.14704/web/v19i1/web19048.
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This paper discusses the improvement of facial detection algorithms using the DCT algorithm and image processing. Face key point is very needed in the face recognition system. Some important factors that have effects to detect its result are noise and illuminations. These two factors can be overcome by eliminating some DCT coefficients, both high and low. However, after handling the problem, most likely the image quality will become decrease, which will adversely influence the performance of the feature detector algorithm. Therefore, it is very important to test the performance of the feature detector algorithm on images that are implemented noise and illumination handling and how to improve the quality again. This research implemented Discrete Cosine Transform (DCT), by eliminating the high and low coefficient because there is noise and illumination. However, it is not known at what coefficient level is the most effective, so testing in this study was carried out. Four deblurring algorithms are tested in this research, Blind Deconvolution, Wiener Filter Deconvolution, Lucy-Richardson Deconvolution, and Regularization Deconvolution. And tested the CLAHE algorithm to overcome the effect of removing low coefficient DCT. The best coefficient value to be removed at the DCT frequency is 0.75 with the best SURF algorithm, without the use of other algorithms. Also, the highest F-score is produced by the SURF detector at removing DCT low frequency in combination with the CLAHE algorithm. With the most ideal coefficient of 0.25.
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BOLCAȘ, Radu-Daniel, and Diana DRANGA. "Facial Emotions Recognition in Machine Learning." Electrotehnica, Electronica, Automatica 69, no. 4 (November 15, 2021): 87–94. http://dx.doi.org/10.46904/eea.21.69.4.1108010.
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Facial expression recognition (FER) is a field where many researchers have tried to create a model able to recognize emotions from a face. With many applications such as interfaces between human and machine, safety or medical, this field has continued to develop with the increase of processing power. This paper contains a broad description on the psychological aspects of the FER and provides a description on the datasets and algorithms that make the neural networks possible. Then a literature review is performed on the recent studies in the facial emotion recognition detailing the methods and algorithms used to improve the capabilities of systems using machine learning. Each interesting aspect of the studies are discussed to highlight the novelty and related concepts and strategies that make the recognition attain a good accuracy. In addition, challenges related to machine learning were discussed, such as overfitting, possible causes and solutions and challenges related to the dataset such as expression unrelated discrepancy such as head orientation, illumination, dataset class bias. Those aspects are discussed in detail, as a review was performed with the difficulties that come with using deep neural networks serving as a guideline to the advancement domain. Finally, those challenges offer an insight in what possible future directions can be taken to develop better FER systems.
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Mamadou, Diarra, Kacoutchy Jean Ayikpa, Abou Bakary Ballo, and Brou Medard Kouassi. "Application of Three Convolutional Neural Network Algorithms for Occluded Face Identification and Recognition for System Security." American Journal of Multidisciplinary Research and Innovation 1, no. 5 (October 25, 2022): 24–32. http://dx.doi.org/10.54536/ajmri.v1i5.740.
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Deep Learning techniques in computer vision have become indispensable elements in biometric systems, especially face recognition. Facial recognition can be reliably used as an identification and authentication tool for premises or network access security. The masks wearing, which is one of the problems of concealment, are nowadays part of our habits for preventing COVID-19 disease, which leads to an obstruction of facial recognition. Occulted face recognition is one of the most challenging problems biometrics deals with. This paper presents convolution neural network algorithms for occluded face recognition. Our study presents a robust method using algorithms such as ResNet-50, VGG-19, and DenseNet-201 to contribute to occluded face recognition. Various parameters are used for this experiment, such as the cross-entropy used as a loss function and optimization algorithms adapted to deep learning. These include the SGD, Adam, and RMSProp optimizers. The convolution neural network algorithms were evaluated on the AR database. This experiment gave results that ranged from 94.81 to 99.81% for SGD, from 0 to 96.92 for Adam, and finally from 0 to 96.92 for RMSProp. DenseNet-201 algorithm using the SGD optimizer obtained the best score with 99.81%, and all the performance metrics used such as accuracy, MSE, F-score, recall, and MCC were used to confirm this good performance.
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Li, Li Sai, Zi Lu Ying, and Bin Bin Huang. "Facial Expression Recognition Based on Gabor Texture Features and Centre Binary Pattern." Applied Mechanics and Materials 742 (March 2015): 257–60. http://dx.doi.org/10.4028/www.scientific.net/amm.742.257.
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This paper was proposed a new algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Centre Binary Pattern (CBP). Firstly, gabor texture feature were extracted from every expression image. Five scales and eight orientations of gabor wavelet filters were used to extract gabor texture features. Then the CBP features were extracted from gabor feature images and adaboost algorithm was used to select final features from CBP feature images. Finally, we obtain expression recognition results on the final expression features by Sparse Representation-based Classification (SRC) method. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm had a much higher recognition rate than the traditional algorithms.
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de Vries, Patricia, and Willem Schinkel. "Algorithmic anxiety: Masks and camouflage in artistic imaginaries of facial recognition algorithms." Big Data & Society 6, no. 1 (January 2019): 205395171985153. http://dx.doi.org/10.1177/2053951719851532.
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This paper discusses prominent examples of what we call “algorithmic anxiety” in artworks engaging with algorithms. In particular, we consider the ways in which artists such as Zach Blas, Adam Harvey and Sterling Crispin design artworks to consider and critique the algorithmic normativities that materialize in facial recognition technologies. Many of the artworks we consider center on the face, and use either camouflage technology or forms of masking to counter the surveillance effects of recognition technologies. Analyzing their works, we argue they on the one hand reiterate and reify a modernist conception of the self when they conjure and imagination of Big Brother surveillance. Yet on the other hand, their emphasis on masks and on camouflage also moves beyond such more conventional critiques of algorithmic normativities, and invites reflection on ways of relating to technology beyond the affirmation of the liberal, privacy-obsessed self. In this way, and in particular by foregrounding the relational modalities of the mask and of camouflage, we argue academic observers of algorithmic recognition technologies can find inspiration in artistic algorithmic imaginaries.