Review of factors affecting facial recognition algorithms performance
Keywords:
ANN, facial recognition, facial detection and recognition, gabor wavelet, LDA, PCAAbstract
The face is a significant part of the human body, recognizing people in large groups of individuals. Thus, because of its uniqueness and universality, it has turned into the most generally utilized and acknowledged biometric technique. Many algorithms have been used by various researchers for face detection and recognition. Research, innovation progression, and applications consolidating face recognition in the last twenty years have grown massively. In this paper, some essential existing approaches which are adjusted with dealing with the issues of face recognition have been presented close by their Face recognition accuracy and the variables capable of debasing the performance of the review. In the first section, various factors that decrease facial detection and recognition accuracy have been researched like posture variety, illumination, aging, facial expressions, etc. While in the second section of the paper, various methods have been examined that attempt to relieve the impact of discussed factors. Various algorithms give various exhibitions in various conditions like enlightenment, noise, posture, and mask change. All the previously mentioned methods are represented briefly to give an overall idea. The motive of the paper is to carry all the various methods to a similar spot and simplify it to review the paper.
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T. Fu, W. Chiu and Y. F. Wang, "Learning guided convolutional neural networks for cross-resolution face recognition," 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), 2017, pp. 1-5
W. W. W. Zou and P. C. Yuen, "Very Low Resolution Face Recognition Problem," in IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 327-340, Jan. 2012
Tin, H.H.K.: Removal of noise by median filtering in image processing. In: 6th Parallel and Soft Computing (PSC 2011) Removal (2011)
Z. Luo, J. Hu, W. Deng and H. Shen, "Deep Unsupervised Domain Adaptation for Face Recognition," 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 2018, pp. 453-457
N. A. Abdullah, M. J. Saidi, N. H. A. Rahman, C. C. Wen, and I. R. A. Hamid, "Face recognition for criminal identification: An implementation of principal component analysis for face recognition", AIP Conference Proceedings, vol. 1891, no. 1, pp. 020002, 2017.
C. Ding, C. Xu and D. Tao, "Multi-Task Pose-Invariant Face Recognition," in IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 980-993, March 2015
P. Wang, W. Lin, K. Chao and C. Lo, "A Face-Recognition Approach Using Deep Reinforcement Learning Approach for User Authentication," 2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), 2017, pp. 183-188
T. Ahonen, A. Hadid and M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, Dec. 2006
S. Z. Li, R. Chu, S. Liao and L. Zhang, "Illumination Invariant Face Recognition Using Near-Infrared Images," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 627-639, April 2007
S. Banerjee et al., "To Frontalize or Not to Frontalize: Do We Really Need Elaborate Pre-processing to Improve Face Recognition?," 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, pp. 20-29
Y. Gao and H. J. Lee, ‘Cross-pose face recognition based on multiple virtual views and alignment error’, Pattern Recognition Letters, vol. 65, pp. 170–176, 2015.
Z. Li, U. Park and A. K. Jain, "A Discriminative Model for Age Invariant Face Recognition," in IEEE Transactions on Information Forensics and Security, vol. 6, no. 3, pp. 1028-1037, Sept. 2011
M. Turk and A. Pentland, "Eigenfaces for Recognition," in Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86
N. Jamil, S. Lqbal and N. Iqbal, "Face recognition using neural networks," Proceedings. IEEE International Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century., 2001, pp. 277-281
A. Reda and B. Aoued, "Artificial neural network-based face recognition," First International Symposium on Control, Communications and Signal Processing, 2004., 2004, pp. 439-442
L. Yuan, Z. Qu, Y. Zhao, H. Zhang and Q. Nian, "A convolutional neural network based on TensorFlow for face recognition," 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2017, pp. 525-529
B. S. Manjunath, R. Chellappa and C. von der Malsburg, "A feature based approach to face recognition," Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1992, pp. 373-378
P. Campadelli, R. Lanzarotti and C. Savazzi, "A feature-based face recognition system," 12th International Conference on Image Analysis and Processing, 2003.Proceedings., 2003, pp. 68-73
A. Khan et al., "Forensic Video Analysis: Passive Tracking System for Automated Person of Interest (POI) Localization," in IEEE Access, vol. 6, pp. 43392-43403, 2018
Z. Huang et al., "A Benchmark and Comparative Study of Video-Based Face Recognition on COX Face Database," in IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5967-5981, Dec. 2015
A. A. Fathima, S. Ajitha, V. Vaidehi, M. Hemalatha, R. Karthigaiveni and R. Kumar, "Hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis," 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS), 2015, pp. 220-225
Z. Lei, C. Wang, Q. Wang and Y. Huang, "Real-Time Face Detection and Recognition for Video Surveillance Applications," 2009 WRI World Congress on Computer Science and Information Engineering, 2009, pp. 168-172
Z. Lei, C. Wang, Q. Wang and Y. Huang, "Real-Time Face Detection and Recognition for Video Surveillance Applications," 2009 WRI World Congress on Computer Science and Information Engineering, 2009, pp. 168-172
R. Singh, M. Vatsa, A. Ross and A. Noore, "A Mosaicing Scheme for Pose-Invariant Face Recognition," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, no. 5, pp. 1212-1225, Oct. 2007
S. Anwarul and S. Dahiya, "A Comprehensive Review on Face Recognition Methods and Factors Affecting Facial Recognition Accuracy", Jan. 2020, pp. 495–514.
P. Borisagar, S. Jani, Y. Agrawal and R. Parekh, "An Efficient and Compact Review of Face Recognition Techniques," 2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS), 2020, pp. 1-5
G. Castaneda and T. M. Khoshgoftaar, "A Review of Performance Evaluation on 2D Face Databases," 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService), 2017, pp. 218
M. S. Ejaz, M. R. Islam, M. Sifatullah and A. Sarker, "Implementation of Principal Component Analysis on Masked and Non-masked Face Recognition," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019, pp. 1-5
H. Fredj, S. Bouguezzi, and C. Souani, "Face recognition in unconstrained environment with CNN", The Visual Computer, vol. 37, Feb. 2021.
H. Aung, A. V. Bobkov and N. L. Tun, "Face Detection in Real Time Live Video Using Yolo Algorithm Based on Vgg16 Convolutional Neural Network," 2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), 2021, pp. 697-702
S. Mhadgut, "Masked Face Detection and Recognition System in Real Time using YOLOv3 to combat COVID-19," 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021, pp. 1-7
S. Gupta, K. Thakur, and M. Kumar, "2D-human face recognition using SIFT and SURF descriptors of face’s feature regions", The Visual Computer, vol. 37, no. 3, pp. 447–456, Μar. 2021.
M. Taskiran, N. Kahraman, and C. Eroglu Erdem, "Hybrid face recognition under adverse conditions using appearance-based and dynamic features of smile expression", IET Biometrics, vol. 10, no. 1, pp. 99–115, 2021
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