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Journal articles on the topic 'Facial recognition'

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Author: Grafiati

Published: 4 June 2021

Last updated: 31 August 2024

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1

Goud, N. Swapna, K. Revanth Reddy, and G. Alekhya G. S. Sucheta. "Facial Emoji Recognition." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1330–33. http://dx.doi.org/10.31142/ijtsrd23166.

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C Karthik Srivatsa, Ashritha. "Facial Recognition Voting System (FRVS)." International Journal of Science and Research (IJSR) 12, no. 2 (February 5, 2023): 1144–46. http://dx.doi.org/10.21275/sr23217142716.

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3

Deshwal, Amit, Mohnish Chandiramani, and Umesh Jagtap Prof Amruta Surana. "Smart Door Access using Facial Recognition." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 442–43. http://dx.doi.org/10.31142/ijtsrd21363.

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4

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.

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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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Ramya, Mrs T., Mr Y. Anurag, Ms K. S. S. Pranathi, Mr M. Samba Raju, and Mr P. Rohith. "FACIAL EMOTION RECOGNITION." YMER Digital 21, no. 04 (April 29, 2022): 543–57. http://dx.doi.org/10.37896/ymer21.04/55.

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Facial Emotion is the noticeable indication of the full of affective state, cognitive activity, intension, personality, and psychopathology of an individual and plays a communicative role in interpersonal relations. Automatic recognition of facial emotion can be a significant part of normal human-machine interfaces; it might likewise be utilized in conduct science and in clinical practice. An automatic Facial Emotion Recognition framework requirement to perform identification and area of countenances in a jumbled scene, facial component extraction, and look characterization. The facial emotion recognition framework is carried out utilizing the Deep Convolution Neural Network (DCNN). The CNN model of the undertaking depends on LeNet Architecture. Kaggle Facial Expression Dataset (FER-2013) with seven facial feelings named as happy, sad, surprise, fear, anger, disgust, and neutral is utilized in this venture. The framework accomplished 65% ± 5% accuracy.

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6

Hughes, Shola, Aimee Hudson, Sandra Jablonska, Matthew Wright, and Sophie Lawson. "Facial Recognition Technology." Student Journal of Professional Practice and Academic Research 3, no. 1 (March 4, 2021): 32. http://dx.doi.org/10.19164/sjppar.v3i1.1104.

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Zuo, Kevin J., Tomas J. Saun, and Christopher R. Forrest. "Facial Recognition Technology." Plastic and Reconstructive Surgery 143, no. 6 (June 2019): 1298e—1306e. http://dx.doi.org/10.1097/prs.0000000000005673.

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8

Benton, Arthur. "Facial Recognition 1990." Cortex 26, no. 4 (December 1990): 491–99. http://dx.doi.org/10.1016/s0010-9452(13)80299-7.

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9

Maurya, Vikas, Ms Anjali Awasthi, Rajat Singh, Deepak Maurya, and Manorma Dwivedi. "Facial Emotion Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 4361–66. http://dx.doi.org/10.22214/ijraset.2023.51265.

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Abstract: Facial emotion recognition (FER) has become an important topic in the fields of computer vision and artificial intelligence due to its great academic and commercial potential. Although FER can be performed using multiple sensors, this review focuses on studies using facial images exclusively, since visual expressions areone of the main channels of information in human communication. Automatic emotion recognition based onfacial expressions is an interesting research area that has been applied and applied in various fields such as safety, health and human-computer interface. Researchersin this field are interested in developing techniquesto interpret, encode facial expressions and extract these features for better prediction by computer. With the remarkable success of deep learning, different types of architectures of this technique are exploited to achieve better performance. The purpose of this paperisto conduct a study ofrecent work on automatic facial emotionrecognition (FER) via deep learning. We highlight these contributions, the architectures and the databases used, and we show the progress achieved by comparing the proposed methods and the obtained results. The purpose of this paper is to serve and guide researchers by reviewing recent work and providing insights to improve the field.

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10

Tiwari, Er Shesh Mani, and Er Mohd Shah Alam. "Facial Emotion Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (February 28, 2023): 490–94. http://dx.doi.org/10.22214/ijraset.2023.49067.

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Abstract: Facial Emotion Recognition plays a significant role in interacting with computers which help us in various fields like medical processes, to present content on the basis of human mood, security and other fields. It is challenging because of heterogeneity in human faces, lighting, orientation, poses and noises. This paper aims to improve the accuracy of facial expression recognition. There has been much research done on the fer2013 dataset using CNN (Convolution Neural Network) and their results are quite impressive. In this work we performed CNN on the fer2013 dataset by adding images to improve the accuracy. To our best knowledge, our model achieves the accuracy of 70.23 % on fer2013 dataset after adding images in training and testing parts of disgusted class.

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11

Mehra, Shivam, Prabhat Parashar, Akshay Aggarwal, and Deepika Rawat. "FACIAL EXPRESSION RECOGNITION." International Journal of Advanced Research 12, no. 01 (January 31, 2024): 1109–13. http://dx.doi.org/10.21474/ijar01/18230.

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Facial Expression Recognition is a system which provides an interface for computer and human interaction. With the advancement of technology and need of the hour such systems have earned the interest of researchers in psychology, medicine, computer science and similar other fields as its applications are identified in such fields. Facial expression recognizer is an application which uses live data coming from the camera or even the existing videos to capture the expressions of the person in the video and is represented on the screen in the form of attractive emojis. Expressions form the basis of human communication and interaction. Expressions are used as a crucial tool to study the behaviour in the medicine and psychological fields to understand the state of mind of the people. The main objective to develop such a system was to be able to classify facial expressions using CNN algorithm which is responsible for the expression detection and then providing a corresponding emoticon relevant to detected facial expression as the output.

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12

Wu, Danning, Shi Chen, Yuelun Zhang, Huabing Zhang, Qing Wang, Jianqiang Li, Yibo Fu, et al. "Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition." Journal of Personalized Medicine 11, no. 11 (November 10, 2021): 1172. http://dx.doi.org/10.3390/jpm11111172.

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Artificial intelligence (AI) technology is widely applied in different medical fields, including the diagnosis of various diseases on the basis of facial phenotypes, but there is no evaluation or quantitative synthesis regarding the performance of artificial intelligence. Here, for the first time, we summarized and quantitatively analyzed studies on the diagnosis of heterogeneous diseases on the basis on facial features. In pooled data from 20 systematically identified studies involving 7 single diseases and 12,557 subjects, quantitative random-effects models revealed a pooled sensitivity of 89% (95% CI 82% to 93%) and a pooled specificity of 92% (95% CI 87% to 95%). A new index, the facial recognition intensity (FRI), was established to describe the complexity of the association of diseases with facial phenotypes. Meta-regression revealed the important contribution of FRI to heterogeneous diagnostic accuracy (p = 0.021), and a similar result was found in subgroup analyses (p = 0.003). An appropriate increase in the training size and the use of deep learning models helped to improve the diagnostic accuracy for diseases with low FRI, although no statistically significant association was found between accuracy and photographic resolution, training size, AI architecture, and number of diseases. In addition, a novel hypothesis is proposed for universal rules in AI performance, providing a new idea that could be explored in other AI applications.

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13

Ahmad, Foysal, Kaushik Roy, Brian O‟Connor, Joseph Shelton, Pablo Arias, Albert Esterline, and Gerry Dozier. "Facial Recognition Utilizing Patch Based Game Theory." International Journal of Machine Learning and Computing 5, no. 4 (August 2015): 334–38. http://dx.doi.org/10.7763/ijmlc.2015.v5.530.

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14

R., Prithiviraj. "Automated Attendance System based on Facial Recognition." Journal of Advanced Research in Dynamical and Control Systems 24, no. 4 (March 31, 2020): 124–32. http://dx.doi.org/10.5373/jardcs/v12i4/20201425.

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15

Chitkara M Shobana, Naman. "Attendance Management System Based on Facial Recognition." International Journal of Science and Research (IJSR) 12, no. 4 (April 5, 2023): 1642–46. http://dx.doi.org/10.21275/sr23425205107.

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16

Shrivastava, Anirudh, Dheeraj Dubey, Mansi Verma, and Himanchal Verma. "Facial Emotion Recognition using Video and Audio." International Journal of Research Publication and Reviews 5, no. 1 (January 8, 2024): 2517–27. http://dx.doi.org/10.55248/gengpi.5.0124.0261.

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17

Wang, Yu Tai, Jie Han, Xiao Qing Jiang, Jing Zou, and Hui Zhao. "Study of Speech Emotion Recognition Based on Prosodic Parameters and Facial Expression Features." Applied Mechanics and Materials 241-244 (December 2012): 1677–81. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1677.

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The present status of speech emotion recognition was introduced in the paper. The emotional databases of Chinese speech and facial expressions were established with the noise stimulus and movies evoking subjects' emtion. For different emotional states, we analyzed the single-mode speech emotion recognitions based the prosodic features and the geometric features of facial expression. Then, we discussed the bimodal emotion recognition by the use of Gaussian Mixture Model. The experimental results show that, the bimodal emotion recognition rate combined with facial expression is about 6% higher than the single model recognition rate merely using prosodic features.

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18

Owusu, Ebenezer, Ebenezer Komla Gavua, and Zhan Yong-Zhao. "Facial Expression Recognition – A Comprehensive Review." International Journal of Technology and Management Research 1, no. 4 (March 12, 2020): 29–46. http://dx.doi.org/10.47127/ijtmr.v1i4.36.

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In this paper, we have provided a comprehensive review of modern facial expression recognition system. The history of the technology as well as the current status in terms of accomplishments and challenges has been emphasized. First, we highlighted some modern applications of the technology. The best methods of face detection, an essential component of automatic facial expression system, are also discussed. Facial Action Coding Systems- the cumulative database of research and development of micro expressions within the behavioral science are also enlightened. Then various facial expression databases and the types of recognitions are explained in detail. Finally, we provided the procedures of facial expression recognition from feature extraction to classifications, emphasizing on modern and best approaches. Then the challenges encountered when comparing results with others are highlighted and suggestions to alleviate the problems, provided. Keywords: FACS; Expression recognition; spatial; spatio-temporal; expression classification

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19

G.Sowmiya and V. Kumutha. "Facial Expression Recognition Using Static Facial Images." International Journal of Scientific Research in Computer Science and Engineering 6, no. 2 (April 2018): 72–75. http://dx.doi.org/10.26438/ijsrcse/v6i2.7275.

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20

Zhang, Ligang, and Dian Tjondronegoro. "Facial Expression Recognition Using Facial Movement Features." IEEE Transactions on Affective Computing 2, no. 4 (October 2011): 219–29. http://dx.doi.org/10.1109/t-affc.2011.13.

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21

Song, K. T., M. J. Han, F. Y. Chang, and S. H. Chang. "A Robotic Facial Expression Recognition System Using Real-Time Vision System." Key Engineering Materials 381-382 (June 2008): 375–78. http://dx.doi.org/10.4028/www.scientific.net/kem.381-382.375.

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The capability of recognizing human facial expression plays an important role in advanced human-robot interaction development. Through recognizing facial expressions, a robot can interact with a user in a more natural and friendly manner. In this paper, we proposed a facial expression recognition system based on an embedded image processing platform to classify different facial expressions on-line in real time. A low-cost embedded vision system has been designed and realized for robotic applications using a CMOS image sensor and digital signal processor (DSP). The current design acquires thirty 640x480 image frames per second (30 fps). The proposed emotion recognition algorithm has been successfully implemented on the real-time vision system. Experimental results on a pet robot show that the robot can interact with a person in a responding manner. The developed image processing platform is effective for accelerating the recognition speed to 25 recognitions per second with an average on-line recognition rate of 74.4% for five facial expressions.

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22

Kamińska, Dorota, Kadir Aktas, Davit Rizhinashvili, Danila Kuklyanov, Abdallah Hussein Sham, Sergio Escalera, Kamal Nasrollahi, Thomas B. Moeslund, and Gholamreza Anbarjafari. "Two-Stage Recognition and beyond for Compound Facial Emotion Recognition." Electronics 10, no. 22 (November 19, 2021): 2847. http://dx.doi.org/10.3390/electronics10222847.

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Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.

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23

Gervasi, Osvaldo, Valentina Franzoni, Matteo Riganelli, and Sergio Tasso. "Automating facial emotion recognition." Web Intelligence 17, no. 1 (February 22, 2019): 17–27. http://dx.doi.org/10.3233/web-190397.

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24

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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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.

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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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Maheshwaran, Dr Uma. "Emotion based Facial Recognition." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (July 31, 2020): 866–71. http://dx.doi.org/10.22214/ijraset.2020.30358.

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27

Ng, Wei-Jen, and R. C. L. Lindsay. "Cross-Race Facial Recognition." Journal of Cross-Cultural Psychology 25, no. 2 (June 1994): 217–32. http://dx.doi.org/10.1177/0022022194252004.

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28

Pampus, Juergen, and Frank Weber. "Facial recognition — An overview." Information Security Technical Report 3, no. 1 (January 1998): 40–46. http://dx.doi.org/10.1016/s1363-4127(98)80017-4.

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Pampus, Juergen, and Frank Weber. "Facial recognition — an overview." Information Security Technical Report 4 (January 1999): 29. http://dx.doi.org/10.1016/s1363-4127(99)80056-9.

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30

Mubashshir, Mr, Kshitij Shinghal, and Manas Singhal. "Review on Facial Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 8 (August 20, 2019): 07–12. http://dx.doi.org/10.17762/ijritcc.v7i8.5345.

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Generally face recognition perform many operations in our daily life such as security purpose identification of people and verification purpose. The basic aim of my project is to design an effective and secure technique for authentication using face recognition that can search or recognize a human face among the thousands of persons and improve the performance of face recognition system in low light conditions and also evaluate the performance of the designed framework by comparing the performance of existing face recognition system. This study also provides a automatic system through which a given still image or video of a scene, identify one or more persons in this scene by using a stored database of facial images.

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Bulsara, Ardashir, and Shashank Jagadeesha. "ARTIFICIAL INTELLIGENCE-FACIAL RECOGNITION." International Journal of Research in Engineering and Technology 6, no. 11 (November 15, 2017): 22–26. http://dx.doi.org/10.15623/ijret.2017.0611005.

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32

R, SARAVANAN,. "FACIAL EMOTION RECOGNITION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 21, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34371.

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Face Emotion recognition play a significance role in fields like aid, border management, police work, banking services, and client product. Facial expressions is wide utilized in social communication since they convey heaps of knowledge regarding folks, like moods, emotions, and alternative things. during this paper, we tend to review facial feeling recognition victimisation CNNs and highlight totally different algorithms and their performance impact. Further, we tend to demonstrate that utilizing CNNs during this field - ends up in a considerable performance increase. By forming associate ensemble of recent deep CNNs, we tend to get a FER2013 take a look at accuracy of 91.2%, outperforming previous works while not requiring auxiliary coaching knowledge or face registration. Key Words: Facial Expression, Confusion Matrix, Emotion Optimizer, Haarcascade classifier

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l Nezam, Mohammad Afzal. "Facial Recognition Attendance System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 22, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34176.

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By identifying students' frontal faces from classroom photos, this research attempts to construct a general face detection and identification system that will automate the process of gathering school attendance. The main issue with conventional attendance management systems is the accuracy of the data that is gathered. Numerous automated techniques are in use, including biometric attendance. Nonetheless, the effectiveness of these methods is always impacted by scanning equipment technical issues. In order to enhance data quality and information accessibility for authorised parties, this article uses OpenCV for face recognition and principal component analysis techniques for face detection. The database that holds user data in the system was developed using SQL, while the Python programming language was utilised to create the suggested system. After testing, it was determined that the new system is safe and secures students' identities by providing an anonymous attendance environment. Keywords: (ABS) Face Detection; Attendance; Machine Learning; Database; Principal component analysis;CNN; OpenCV and Face Recognition

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Alam, Md Azad. "FACIAL RECOGNITION ATTENDANCE SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (March 22, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem29448.

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Colleges have historically faced a great deal of difficulty with student attendance, necessitating a large time and effort investment from staff in manual tracking. Even though they are in place, the existing biometric attendance systems are not entirely automated, which causes delays in processing fingerprints, maintenance issues, and inefficiencies in time. Given that almost everyone has a smartphone and is continuously online in this day and age, a more simplified method is necessary. This study suggests using sophisticated object identification algorithms to check attendance using faculty members' smartphones. Because of its effectiveness in face detection and the addition of Microsoft Azure's face API for database recognition, YOLO V3 (You Only Look Once) is the preferred option among these. One special feature of the system is that it takes pictures of the classroom at the start and finish of every class to make sure everyone is present. After determining the number of students in each photograph, YOLO V3 separates the faces that are known and those that are unknown, creating distinct spreadsheets. Monthly email reminders are also sent to teachers, parents, and students. The system that has been put into place shows strong real-time performance in counting and detecting jobs, with excellent facial recognition accuracy and overall efficiency. Keywords:, OpenCV, Local Binary Pattern Histogram (LBPH), Real-time Tracking, Facial Analysis, You Only Look Once (YOLO V3), Firebase Database.

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Gopal, Jagadeesh. "An Approach for Facial Recognition Using Deep Learning." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (February 28, 2020): 137–43. http://dx.doi.org/10.5373/jardcs/v12sp3/20201247.

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Jiang, H., K. Huang, T. Mu, R. Zhang, T. O. Ting, and C. Wang. "Robust One-Shot Facial Expression Recognition with Sunglasses." International Journal of Machine Learning and Computing 6, no. 2 (April 2016): 80–86. http://dx.doi.org/10.18178/ijmlc.2016.6.2.577.

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37

Guojiang, Wang, and Yang Guoliang. "Facial Expression Recognition Using PCA and AdaBoost Algorithm." International Journal of Signal Processing Systems 7, no. 2 (March 2019): 73–77. http://dx.doi.org/10.18178/ijsps.7.2.73-77.

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Kuttenreich, Anna-Maria, Harry von Piekartz, and Stefan Heim. "Is There a Difference in Facial Emotion Recognition after Stroke with vs. without Central Facial Paresis?" Diagnostics 12, no. 7 (July 15, 2022): 1721. http://dx.doi.org/10.3390/diagnostics12071721.

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The Facial Feedback Hypothesis (FFH) states that facial emotion recognition is based on the imitation of facial emotional expressions and the processing of physiological feedback. In the light of limited and contradictory evidence, this hypothesis is still being debated. Therefore, in the present study, emotion recognition was tested in patients with central facial paresis after stroke. Performance in facial vs. auditory emotion recognition was assessed in patients with vs. without facial paresis. The accuracy of objective facial emotion recognition was significantly lower in patients with vs. without facial paresis and also in comparison to healthy controls. Moreover, for patients with facial paresis, the accuracy measure for facial emotion recognition was significantly worse than that for auditory emotion recognition. Finally, in patients with facial paresis, the subjective judgements of their own facial emotion recognition abilities differed strongly from their objective performances. This pattern of results demonstrates a specific deficit in facial emotion recognition in central facial paresis and thus provides support for the FFH and points out certain effects of stroke.

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Supriyanto, Supriyanto, Maisevli Harika, Maya Sri Ramadiani, and Diena Rauda Ramdania. "Multiscale Retinex Application to Analyze Face Recognition." Jurnal Online Informatika 5, no. 2 (December 8, 2020): 217. http://dx.doi.org/10.15575/join.v5i2.668.

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The main challenge that facial recognition introduces is the difficulty of uneven lighting or dark tendencies. The image is poorly lit, which makes it difficult for the system to perform facial recognition. This study aims to normalize the lighting in the image using the Multiscale Retinex method. This method is applied to a face recognition system based on Principal Component Analysis to determine whether this method effectively improves images with uneven lighting. The results showed that the Multiscale Retinex approach to face recognition's correctness was better, from 40% to 76%. Multiscale Retinex has the advantage of dark facial image types because it produces a brighter image output.

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40

Wang, Xiao, and Yan Li. "Facial Recognition System Based on Genetic Algorithm Improved ROI-KNN Convolutional Neural Network." Applied Bionics and Biomechanics 2022 (October 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/7976856.

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The facial recognition system is an application tool that uses artificial intelligence technology and biometrics technology to analyze and recognize the facial feature information of the human face. It is widely used in various fields, such as attendance and access control management in schools and companies, identity monitoring in stations and stores, facial recognition for fugitive criminals, and facial payment on mobile terminals. However, due to the short development time of the facial recognition system, the facial recognition system has the problem of low recognition accuracy when the recognized object is not cooperative. Although some scholars have proposed the region of interest (ROI)-K nearest neighbor algorithm (KNN) convolutional neural network theory by using the ROI and KNN and applied it to face recognition, the facial recognition system based on ROI-KNN convolutional neural network did not solve the problems of insufficient facial recognition accuracy and insufficient security. Under the conditions of insufficient illumination, excessive expression change, occlusion, high similarity of different individuals, and dynamic recognition, the recognition effect of the facial recognition system based on the ROI-KNN convolutional neural network is relatively limited. Therefore, to make the recognition accuracy of the facial recognition system higher and to make the facial recognition system play a greater role in the social and economic fields, this paper used the adaptive quantum genetic algorithm, the improved marker line graph genetic algorithm, and the feature weight value genetic algorithm to study the facial recognition system of the ROI-KNN convolutional neural network. The research results showed that after improving the ROI-KNN convolutional neural network based on the genetic algorithm, the recognition accuracy of the facial recognition system was increased by 4.99%, the recognition speed was increased by 7.46%, and the recognition security was increased by 2.66%.

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41

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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Kuttenreich, Anna-Maria, Gerd Fabian Volk, Orlando Guntinas-Lichius, Harry von Piekartz, and Stefan Heim. "Facial Emotion Recognition in Patients with Post-Paralytic Facial Synkinesis—A Present Competence." Diagnostics 12, no. 5 (May 4, 2022): 1138. http://dx.doi.org/10.3390/diagnostics12051138.

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Facial palsy is a movement disorder with impacts on verbal and nonverbal communication. The aim of this study is to investigate the effects of post-paralytic facial synkinesis on facial emotion recognition. In a prospective cross-sectional study, we compared facial emotion recognition between n = 30 patients with post-paralytic facial synkinesis (mean disease time: 1581 ± 1237 days) and n = 30 healthy controls matched in sex, age, and education level. Facial emotion recognition was measured by the Myfacetraining Program. As an intra-individual control condition, auditory emotion recognition was assessed via Montreal Affective Voices. Moreover, self-assessed emotion recognition was studied with questionnaires. In facial as well as auditory emotion recognition, on average, there was no significant difference between patients and healthy controls. The outcomes of the measurements as well as the self-reports were comparable between patients and healthy controls. In contrast to previous studies in patients with peripheral and central facial palsy, these results indicate unimpaired ability for facial emotion recognition. Only in single patients with pronounced facial asymmetry and severe facial synkinesis was an impaired facial and auditory emotion recognition detected. Further studies should compare emotion recognition in patients with pronounced facial asymmetry in acute and chronic peripheral paralysis and central and peripheral facial palsy.

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Li, Shuangyan, Min Ji, Ming Chen, and Lanzhi Chen. "Facial length and angle feature recognition for digital libraries." PLOS ONE 19, no. 7 (July 24, 2024): e0306250. http://dx.doi.org/10.1371/journal.pone.0306250.

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With the continuous progress of technology, facial recognition technology is widely used in various scenarios as a mature biometric technology. However, the accuracy of facial feature recognition has become a major challenge. This study proposes a face length feature and angle feature recognition method for digital libraries, targeting the recognition of different facial features. Firstly, an in-depth study is conducted on the architecture of facial action networks based on attention mechanisms to provide more accurate and comprehensive facial features. Secondly, a network architecture based on length and angle features of facial expressions, the expression recognition network is explored to improve the recognition rate of different expressions. Finally, an end-to-end network framework based on attention mechanism for facial feature points is constructed to improve the accuracy and stability of facial feature recognition network. To verify the effectiveness of the proposed method, experiments were conducted using the facial expression dataset FER-2013. The experimental results showed that the average recognition rate for the seven common expressions was 97.28% to 99.97%. The highest recognition rate for happiness and surprise was 99.97%, while the relatively low recognition rate for anger, fear, and neutrality was 97.18%. The data has verified that the research method can effectively recognize and distinguish different facial expressions, with high accuracy and robustness. The recognition method based on attention mechanism for facial feature points has effectively optimized the recognition process of facial length and angle features, significantly improving the stability of facial expression recognition, especially in complex environments, providing reliable technical support for digital libraries and other fields. This study aims to promote the development of facial recognition technology in digital libraries, improve the service quality and user experience of digital libraries.

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Snehitha, Kanaparthi. "Facial Expression Recognition with Appearance Based Features of Facial Landmarks." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3343–48. http://dx.doi.org/10.22214/ijraset.2021.35702.

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Artificial intelligence technology has been trying to bridge the gap between humans and machines. The latest development in this technology is Facial recognition. Facial recognition technology identifies the faces by co-relating and verifying the patterns of facial contours. Facial recognition is done by using Viola-Jones object detection framework. Facial expression is one of the important aspects in recognizing human emotions. Facial expression also helps to determine interpersonal relation between humans. Automatic facial recognition is now being used very widely in almost every field, like marketing, health care, behavioral analysis and also in human-machine interaction. Facial expression recognition helps a lot more than facial recognition. It helps the retailers to understand their customers, doctors to understand their patients, and organizations to understand their clients. For the expression recognition, we are using the landmarks of face which are appearance-based features. With the use of an active shape model, LBP (Local Binary Patterns) derives its properties from face landmarks. The operation is carried out by taking into account pixel values, which improves the rate of expression recognition. In an experiment done using previous methods and 10-fold cross validation, the accuracy achieved is 89.71%. CK+ Database is used to achieve this result.

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Medioni, Gerard, Jongmoo Choi, Matthieu Labeau, Jatuporn Toy Leksut, and Lingchao Meng. "3D Facial Landmark Tracking and Facial Expression Recognition." Journal of information and communication convergence engineering 11, no. 3 (September 30, 2013): 207–15. http://dx.doi.org/10.6109/jicce.2013.11.3.207.

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Yoshimori, Seiki, Hironori Takimoto, Yasue Mitsukura, and Minoru Fukumi. "Facial Impression Recognition Based on Facial Texture Information." Journal of Signal Processing 16, no. 5 (2012): 419–25. http://dx.doi.org/10.2299/jsp.16.419.

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Yongqiang Li, Shangfei Wang, Yongping Zhao, and Qiang Ji. "Simultaneous Facial Feature Tracking and Facial Expression Recognition." IEEE Transactions on Image Processing 22, no. 7 (July 2013): 2559–73. http://dx.doi.org/10.1109/tip.2013.2253477.

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Jia, Shaoxue, and Jingxin Zhang. "Legal protection of a citizen’s right to a facial image when applying facial recognition technology in Chinese law." Pravovedenie 68, no. 2 (2024): 271–83. http://dx.doi.org/10.21638/spbu25.2024.210.

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In the era of the rapid development of big data, facial recognition biometric technology, which identifies and confirms the identity of persons by their face, is gradually becoming an object of public attention. Developing facial recognition technology has become an indispensable means of collecting information for government and commercial organizations. Facial recognition technology can be used to quickly identify individuals and improve efficiency and accuracy. However, there are two sides to everything, and the widespread use of facial recognition technology has affected the traditional system of protecting the citizen’s right to a facial image, and the relevant laws should be improved in connection with the leakage and misuse of facial images. Only under the double safeguard, both the advancement of facial recognition technology and the improvement of laws can further protect the citizen’s right to a facial image. This article analyzes the use of facial recognition technology and the risks associated with it, explains the legislative framework and its shortcomings in China, and makes appropriate proposals for further protection of the generated facial image information in facial recognition technology.

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Mohinabonu, Agzamova. "ENHANCING FACIAL RECOGNITION THROUGH CONTRASTIVE CONVOLUTION: A COMPREHENSIVE METHODOLOGY." American Journal of Engineering and Technology 5, no. 11 (November 1, 2023): 105–14. http://dx.doi.org/10.37547/tajet/volume05issue11-15.

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This study presents an innovative approach to enhance facial recognition technology using contrastive convolutional neural networks (CNNs). The primary focus is on improving the accuracy and efficiency of face recognition systems under varying conditions. Key elements of this approach include meticulous data preparation and preprocessing, where images undergo normalization and diverse augmentation techniques to ensure quality inputs. The network architecture is designed to process pairs of face images, utilizing a common feature extractor and cascaded convolution layers for detailed feature representation. A specialized kernel generator further refines the process, emphasizing unique facial characteristics. The core of the training regimen is a contrastive loss function, optimized through gradient descent to enhance the network's discriminatory capabilities. Results from the study demonstrate a significant improvement in recognition accuracy, particularly highlighted by the superior performance of the proposed model in comparison to standard facial recognition algorithms. This research provides a comprehensive methodology that could revolutionize face recognition technology, offering more reliable and efficient solutions for various applications.

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Lee, Se A., Jin Kim, Jeon Mi Lee, Yu-Jin Hong, Ig-Jae Kim, and Jong Dae Lee. "Automatic Facial Recognition System Assisted-facial Asymmetry Scale Using Facial Landmarks." Otology & Neurotology 41, no. 8 (July 3, 2020): 1140–48. http://dx.doi.org/10.1097/mao.0000000000002735.

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