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Готові списки джерел за темами / FACE RECOGNITION TECHNIQUES / Статті в журналах

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Статті в журналах з теми "FACE RECOGNITION TECHNIQUES"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Priya, B. Lakshmi, and Dr M. Pushpa Rani Rani. "Face Recognition System Techniques and Approaches." Indian Journal of Applied Research 4, no. 4 (October 1, 2011): 109–13. http://dx.doi.org/10.15373/2249555x/apr2014/32.

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2

Romanyuk, Olexandr N., Sergey I. Vyatkin, Sergii V. Pavlov, Pavlo I. Mykhaylov, Roman Y. Chekhmestruk, and Ivan V. Perun. "FACE RECOGNITION TECHNIQUES." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 10, no. 1 (March 30, 2020): 52–57. http://dx.doi.org/10.35784/iapgos.922.

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Анотація:

The problem of face recognition is discussed. The main methods of recognition are considered. The calibrated stereo pair for the face and calculating the depth map by the correlation algorithm are used. As a result, a 3D mask of the face is obtained. Using three anthropomorphic points, then constructed a coordinate system that ensures a possibility of superposition of the tested mask.

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3

Kushwah, Prashant. "FACE RECOGNITION WITH HYBRID TECHNIQUES." International Journal of Engineering Technologies and Management Research 5, no. 2 (May 1, 2020): 178–87. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.642.

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Face recognition framework is still in test by numerous applications particularly in close perception and in security frameworks. Generally all utilizations of face recognition utilize enormous information sets, making challenges in present time preparing and effectiveness. This paper contains a structure to enhance face recognition framework which have a few phases. For good result in face recognition framework a few upgrades are critical at each stage. A novel plan is displayed in this paper which gives the better execution for face recognition framework. This plan incorporates expanding in datasets, particularly huge datasets which are required for profound learning. Changing the picture differentiate proportion and pivoting the picture at a few edges which can enhance the recognition precision. At that point, trimming the proper territory of face for highlight extraction and getting the best element vector for face recognition finally. The last after effect of this plan will demonstrate that the given structure is able for distinguishing and perceiving faces with various postures, foundations, and appearance in genuine or present time.

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4

jayakumari, V. Vi. "Face Recognition Techniques: A Survey." World Journal of Computer Application and Technology 1, no. 2 (September 2013): 41–50. http://dx.doi.org/10.13189/wjcat.2013.010204.

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5

Kaur, Mandeep, and Jasjit Kaur. "Review of Face Recognition Techniques." International Journal of Computer Applications 164, no. 6 (April 17, 2017): 31–35. http://dx.doi.org/10.5120/ijca2017913731.

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6

Taqdir, Taqdir, and Dr Renu Dhir. "Face Recognition Techniques - A Review." IOSR Journal of Computer Engineering 16, no. 2 (2014): 23–27. http://dx.doi.org/10.9790/0661-162102327.

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7

Somani, Ms Jayashree S. "Face Recognition Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (June 30, 2019): 2498–502. http://dx.doi.org/10.22214/ijraset.2019.6420.

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8

Noori Hashim, Asaad, and Nedaa Kream Shalan. "Face Recognition using Hybrid Techniques." Journal of Engineering and Applied Sciences 14, no. 12 (December 10, 2019): 4158–63. http://dx.doi.org/10.36478/jeasci.2019.4158.4163.

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9

Solanki, Kamini, and Prashant Pittalia. "Review of Face Recognition Techniques." International Journal of Computer Applications 133, no. 12 (January 15, 2016): 20–24. http://dx.doi.org/10.5120/ijca2016907994.

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10

Kachare, Nilima B., and Vandana S. Inamdar. "Survey of Face Recognition Techniques." International Journal of Computer Applications 1, no. 19 (February 25, 2010): 30–34. http://dx.doi.org/10.5120/408-604.

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11

Dhoriya, Narendra R., and Devang U. Shah. "Compression Techniques and Face Recognition using compressed images: A Review." International Journal of Scientific Research 3, no. 5 (June 1, 2012): 142–44. http://dx.doi.org/10.15373/22778179/may2014/43.

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12

S. Vijayalakshmi, J. Uma Maheswari, and K. Jananiyie. "Face Detection and Recognition using Machine Learning Techniques." Journal of Innovative Image Processing 4, no. 4 (January 27, 2023): 316–27. http://dx.doi.org/10.36548/jiip.2022.4.008.

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Анотація:

Face recognition of persons has received so much attention in the recent years due to its many applications in different fields such as security applications, video surveillance, biometric systems, identifying the criminals etc. This paper develops a system that can recognize the human face in the input image after it has been detected. The system is trained with set of faces and non faces, and when the input picture is given, the face is detected using Viola Jones Algorithm. In face recognition, the features are extracted from the training dataset using Principal Component Analysis (PCA) and then the system is trained to recognize the face using Support Vector Machine (SVM) classification. When the input image is given for face recognition, features are extracted from the input picture using PCA and multiclass classification is done by SVM.

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13

Ibrahim, Laheeb, and Ibrahim Saleh. "Face Recognition using Artificial Intelligent Techniques." AL-Rafidain Journal of Computer Sciences and Mathematics 6, no. 2 (July 1, 2009): 211–27. http://dx.doi.org/10.33899/csmj.2009.163809.

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14

Kumar, Rajendra, Papendra Kumar, and Abhishek Gupta. "Review Paper on Face Recognition Techniques." International Journal of Computer Sciences and Engineering 7, no. 5 (May 31, 2019): 223–29. http://dx.doi.org/10.26438/ijcse/v7i5.223229.

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15

Jafri, Rabia, and Hamid R. Arabnia. "A Survey of Face Recognition Techniques." Journal of Information Processing Systems 5, no. 2 (June 30, 2009): 41–68. http://dx.doi.org/10.3745/jips.2009.5.2.041.

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16

Daniel, Neenu. "A Review on Face Recognition Techniques." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 4992–98. http://dx.doi.org/10.22214/ijraset.2018.4813.

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17

Ingale, Shivani. "Face Recognition using Video framing Techniques." International Journal for Research in Applied Science and Engineering Technology 7, no. 4 (April 30, 2019): 3193–96. http://dx.doi.org/10.22214/ijraset.2019.4535.

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18

Juliet Anesha, S. R. "A Review of Face Recognition Techniques." Journal of excellence in Computer Science and Engineering 2, no. 2 (August 23, 2016): 10–17. http://dx.doi.org/10.18831/djcse.in/2016021002.

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19

Razzaq, Ali Nadhim, Rozaida Ghazali, Nidhal Khdhair El Abbadi, and Mohammad Dosh. "A Comprehensive Survey on Face Detection Techniques." Webology 19, no. 1 (January 20, 2022): 613–28. http://dx.doi.org/10.14704/web/v19i1/web19044.

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Анотація:

The need for automatic understanding and examination of data increased with the tremendous growth of video and imaging databases. The change of identity, feelings and attitudes of a person's face always play a key role in terms of social communication. It is difficult for human beings to distinguish and identify various faces. Hence, we can say that in face recognition, the automatic computer-aided face detection system plays an important role. It also plays a significant role in determining the facial expressions and their recognition, estimation of head pose and interaction of humans and computers, etc. The size and location of the human face in a digital image are determined by face detection. For face detection in digital images, this paper brings forward a detailed and comprehensive survey of various important techniques. In this paper, different challenges and applications regarding face detection are also discussed. The standard databases for the detection of the face are mentioned along with various other features. Along with this, special discussions are provided regarding highly practical aspects for the robustness of the system for face detection. In the end, there ¬are some highly promising directions for the research and investigation to be conducted in the future.

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20

Dubey, Meeta, and Prashant Jain. "Face Recognition using PCA and LDA Technique for Noisy Faces." International Journal of Electrical and Electronics Research 1, no. 2 (September 30, 2013): 22–28. http://dx.doi.org/10.37391/ijeer.010201.

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Анотація:

Face recognition is always a popular area of research. There are various techniques used in the face recognition system. Principal component analysis (PCA) and linear discriminate analysis (LDA) techniques are the two most well-known techniques for the face recognition. In this paper, the PCA and LDA technique based face recognition system are described. The performance of this technique is compare in term of PSNR and RMSE for noisy image. The Euclidean distance between feature templates and database futures are used for identifying the face image. There are basically three types of noises present, but in this paper I am going to compare the salt and pepper noise with the Gaussian noise in the detailed and analytical ways. After finding the features of the different noisy images I am going to compare both the PCA and LDA technique for the noisy pictures.

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21

Kortli, Yassin, Maher Jridi, Ayman Al Falou, and Mohamed Atri. "Face Recognition Systems: A Survey." Sensors 20, no. 2 (January 7, 2020): 342. http://dx.doi.org/10.3390/s20020342.

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Анотація:

Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

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22

Brahnam, Sheryl, and Loris Nanni. "Predicting trait impressions of faces using local face recognition techniques." Expert Systems with Applications 37, no. 7 (July 2010): 5086–93. http://dx.doi.org/10.1016/j.eswa.2009.12.002.

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23

Chihaoui, Mejda, Akram Elkefi, Wajdi Bellil, and Chokri Ben Amar. "A Survey of 2D Face Recognition Techniques." Computers 5, no. 4 (September 28, 2016): 21. http://dx.doi.org/10.3390/computers5040021.

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24

Bhosale, Sudeshna, Ghatage Dhanashri, and Mane Namrata. "Face Detection and Recognition Techniques: A Survey." IJARCCE 7, no. 11 (November 30, 2018): 207–15. http://dx.doi.org/10.17148/ijarcce.2018.71145.

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25

Kumar, Ishita. "SECURING SMART HOMES USING FACE RECOGNITION TECHNIQUES." International Journal of Advanced Research in Computer Science 9, no. 2 (April 20, 2018): 280–83. http://dx.doi.org/10.26483/ijarcs.v9i2.5717.

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26

Abiyev, Rahib H. "FACIAL FEATURE EXTRACTION TECHNIQUES FOR FACE RECOGNITION." Journal of Computer Science 10, no. 12 (December 1, 2014): 2360–65. http://dx.doi.org/10.3844/jcssp.2014.2360.2365.

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27

Wang, Qiangchang, and Guodong Guo. "Benchmarking deep learning techniques for face recognition." Journal of Visual Communication and Image Representation 65 (December 2019): 102663. http://dx.doi.org/10.1016/j.jvcir.2019.102663.

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28

Shyam, Radhey, and Yogendra Narain Singh. "Identifying Individuals Using Multimodal Face Recognition Techniques." Procedia Computer Science 48 (2015): 666–72. http://dx.doi.org/10.1016/j.procs.2015.04.150.

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29

Desai, Khyati Jash, and Sunil Kumar. "A Study on Face Recognition and Face Spoofing Detection Techniques." International Journal of Computer Applications 185, no. 14 (June 20, 2023): 24–29. http://dx.doi.org/10.5120/ijca2023922823.

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30

Nigam, Harshit. "Review of Facial Recognition Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1740–43. http://dx.doi.org/10.22214/ijraset.2022.40077.

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Abstract: Facial Recognition, the biggest breakthrough in Biometric identification and security since fingerprints, uses an individual’s facial features to identify and recognize them. A technology that seems too farfetched taken straight from a science fiction novel is now available in smartphones in the palm of our hands. Facial Recognition has gained traction as the primary method of identification whether its mobile phones, smart security systems, ID verification or something as simple as login in a website. Recent strides in facial recognition technologies have made it possible to design, build and implement a facial recognition system ourself. Using Computer Vision and machine learning libraries like Facial Recognition and Dlib, we can create a robust system that can detect faces and then match and identify it with a database of pre-loaded facial data to successfully recognize them. This study conducted a literature review of these aforementioned technologies and various other advancements in the field of computer vision facial recognition by other scholars in their research papers. This paper analyzes domains to understand the working of these machine learning models and their different implementations in facial recognition systems. The research conducted by us during this review will be paramount in creating a proof-of-concept prototype facial recognition system. Keywords: DLib, Facial _Recognition, Machine Learning (ML), Deep Learning (DL), CNN, KNN, Face Detection, HOG, Support Vector Machine (SVM), Face Recognition.

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31

SunitaDevi, Ningthoujam, and K. Hemachandran. "Automatic Face Recognition System using Pattern Recognition Techniques: A Survey." International Journal of Computer Applications 83, no. 5 (December 18, 2013): 10–13. http://dx.doi.org/10.5120/14443-2602.

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32

Reddy, Manjunath, Anusha Bodepudi, Mounika Mandapuram, and Sai Srujan Gutlapalli. "Face Detection and Recognition Techniques through the Cloud Network: An Exploratory Study." ABC Journal of Advanced Research 9, no. 2 (December 31, 2020): 103–14. http://dx.doi.org/10.18034/abcjar.v9i2.660.

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Анотація:

Face recognition is one of the fundamental functions performed by biometrics, and it is becoming increasingly influential as new technologies like the internet and digital cameras require improved security critical features. Other applications also make use of face recognition. Face recognition software can work with static photos or visual sequences to accomplish tasks. In addition, it can handle either one of the following tasks: face identification (also known as face recognition) or face verification (also known as face authentication). People can quickly and reliably recognize known faces and identities, even when presented with challenging viewing conditions such as changing illuminations, occlusion, scale, or rotation. This ability is a hallmark of the human species. Motivated by its significance in human-to-human communication and leading to various applications, ranging from biometrics to human-computer interaction, the face recognition challenge is an essential issue in the field of computer vision as well as other related areas. Finally, this article provides a summary of the most recent and cutting-edge strategies that have been developed to deal with challenging tasks like the one being discussed.

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33

Et. al., Dr M. Aruna Safali. "Recognition for lateral faces using Neural Networks." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 10, 2021): 5589–95. http://dx.doi.org/10.17762/turcomat.v12i3.2228.

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Face recognition is most difficult and complicated technique. Recognition of lateral faces is very difficult compare with normal face recognition. Pattern recognition is mostly used in this system to recognise the lateral face patterns (LFP). Neural network is used to find the patterns and lateral face recognition can be done by this technique. After the many researches face recognition becomes difficulty for the various techniques based on their parameters. In this paper, the amalgamative lateral face recognition(ALFR) which is merged with machine learning and neural network features can be done by using synthetic dataset consists of 200 lateral faces. Performance shows the improved results of proposed technique.

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34

Vimal, Chamandeep. "Face Detection’s Various Techniques and Approaches: A Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 839–43. http://dx.doi.org/10.22214/ijraset.2022.39890.

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Abstract: In the past few years, face recognition owned significant consideration and is appreciated as one of the most promising applications in the field of image analysis. Verification and Identification have become a significant issue in the present computerized world. Various variabilities are present across human faces such as pose, expression, position and orientation, skin colour, the presence of glasses or facial hair, variations in camera gain, lighting conditions, and image resolution, because of these variabilities face detection is very complicated. In this paper, several existing face detection methods and strategies are analyzed and studied. The main goal of this paper is to present or suggest an approach that is an excellent choice for face detection. Keywords: Face detection, Recognition, CPU, Multiple layer

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35

YAN, HONG. "HUMAN FACE IMAGE PROCESSING TECHNIQUES." International Journal of Image and Graphics 01, no. 02 (April 2001): 197–215. http://dx.doi.org/10.1142/s021946780100013x.

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Анотація:

Human face image processing techniques have many applications, such as in security operations, entertainment, medical imaging and telecommunications. In this paper, we provide an overview of existing computer algorithms for face detection and facial feature location, face recognition, image compression and animation. We also discuss limitations of current methods and research work needed in the future.

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36

Saraf, Santosh S., Gururaj R. Udupi, and Santosh D. Hajare. "Diagnosis of Esophagitis Based on Face Recognition Techniques." Open Medical Informatics Journal 4, no. 1 (May 28, 2010): 58–62. http://dx.doi.org/10.2174/1874431101004020058.

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Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application of these face recognition techniques for classification of medical images. The motivating factor being, given a condition of an organ it is represented by some typical features. In this paper we report the use of the face recognition techniques to classify the type of Esophagitis, a condition of inflammation of the esophagus. The image of the esophagus is captured in the process of endoscopy. We test PCA, Fisher Face method and Independent Component Analysis techniques to classify the images of the esophagus. Esophagitis is classified into four categories. The results of classification for each method are reported and the results are compared.

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37

Mishra, Namrata, H. Karthikeyan, and Tanya Jayantix. "Face Recognition System for Real Life Applications using Augmentation Techniques." Journal of Innovative Image Processing 5, no. 1 (March 2023): 47–58. http://dx.doi.org/10.36548/jiip.2023.1.004.

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A facial recognition system may recognise a person by comparing his face to the ones in a database of faces taken from a digital image or video frame. It is primarily used to verify the identity of users by analysing facial features from an image. According to several studies, facial recognition technology has made enormous strides with the growth of science and technology, but there is still opportunity for its improvement in practical use. Some issues like rotation, occlusion, and meta learning approach can be handled for improved model accuracy by using 3D technologies like image augmentation to supplement 2D images. Today's facial recognition technology can be used for more than only identifying people; it can also be used for security purposes in residential, business, and commercial applications. Due to its convenience, face recognition technology is widely employed in the financial sector as well. This research proposes face recognition for real life applications using Siamese network and Deep Neural Network, Facenet.

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38

Priyanka Anand, Monika Hooda. "Face Recognition LBP Feature Extraction and PCA Techniques." International Journal of Scientific Research and Management (IJSRM) 5, no. 8 (August 9, 2017): 6624–29. http://dx.doi.org/10.18535/ijsrm/v5i8.08.

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Face recognition is one of the security methods in biometric system in world wide area. Different mathematical calculations are used to perform face recognition. In this paper, Linear Binary pattern is applied to the different ORL face database. Linear discriminant analysis is applied followed by linear binary pattern. In this, different component analysis and training set data reduction is done by PCA techniques. The Feature extraction & pattern recognition is done by LBP method. The classification rate is achieved up to 94.7

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39

Sharma, R., and M. S. Patterh. "A broad review about face recognition – feature extraction and recognition techniques." Imaging Science Journal 63, no. 7 (September 2015): 361–77. http://dx.doi.org/10.1179/1743131x14y.0000000071.

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40

Rani, Sheela, Vuyyuru Tejaswi, Bonthu Rohitha, and Bhimavarapu Akhil. "Pre filtering techniques for face recognition based on edge detection algorithm." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 213. http://dx.doi.org/10.14419/ijet.v7i1.1.9469.

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Анотація:

Recognition of face has been turned out to be the most important and interesting area in research. A face recognition framework is a PC application that is apt for recognizing or confirming the presence of human face from a computerized picture, from the video frames etc. One of the approaches to do this is by matching the chosen facial features with the pictures in the database. It is normally utilized as a part of security frameworks and can be implemented in different biometrics, for example, unique finger impression or eye iris acknowledgment frameworks. A picture is a mix of edges. The curved line potions where the brightness of the image change intensely are known as edges. We utilize a similar idea in the field of face-detection, the force of facial colours are utilized as a consistent value. Face recognition includes examination of a picture with a database of stored faces keeping in mind the end goal to recognize the individual in the given input picture. The entire procedure covers in three phases face detection, feature extraction and recognition and different strategies are required according to the specified requirements.

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41

Pratap, Neeraj, Shwetank Shwetank, and Vikesh Kumar. "Classification of Imagery Data and Face Recognition Techniques." International Journal of Computer Applications 85, no. 10 (January 16, 2014): 21–26. http://dx.doi.org/10.5120/14876-3272.

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42

S. Saraf, Santosh. "Diagnosis of Esophagitis Based on Face Recognition Techniques." Open Medical Informatics Journal 4, no. 1 (May 28, 2010): 58–62. http://dx.doi.org/10.2174/1874431101004010058.

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43

Saraf, Santosh. "Diagnosis of Esophagitis Based on Face Recognition Techniques." Open Medical Informatics Journal 4, no. 1 (August 31, 2010): 58–62. http://dx.doi.org/10.2174/1874325001004010058.

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44

Chauhan, Manish. "Comparative Analysis of Face Recognition Techniques: An Overview." International Journal for Research in Applied Science and Engineering Technology 6, no. 1 (January 31, 2018): 547–52. http://dx.doi.org/10.22214/ijraset.2018.1082.

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