Utilizing Machine Learning to Recognize Faces: A Review
Keywords:
Face recognition, PCA, Viola-Jones, KLT algorithm, EmguCVAbstract
These days, one of the top computer vision technologies is facial recognition. The task of face recognition in computer vision, illumination position, and facial expression is always quite challenging. Target objects are tracked using face recognition in real-time video images captured with a video camera. To put it simply, it is a system program used to recognize a person automatically from a still image or video frame. In this article, we used different algorithms for face recognition, like the Viola-Jones algorithm, PCA algorithm, KLT algorithm, and EmguCV. There are various techniques through which we can perform the above techniques. The present work here depicts the details of various technologies which have been researched and discovered. Let us take an example of designing colour filters that we use in cameras to make them more colorimetric.
References
Ohol MR, Ohol MS. Linear Discriminant Analysis for Human Face Recognition. Int Res J Eng Technol (IRJET). 2017; 4(8): 1–3.
Liu Z, Wang Y. Face detection and tracking in video using dynamic programming. In Proceedings 2000 IEEE International Conference on Image Processing (Cat. No. 00CH37101). 2000 Sep 10; 1: 53–56.
Boda R, Priyadarsini MJ, Pemeena J. Face detection and tracking using KLT and Viola Jones. ARPN J Eng Appl Sci. 2016 Dec; 11(23): 13472–6.
Turk M, Pentland A. Eigenfaces for recognition. J Cogn Neurosci. 1991 Jan 1; 3(1): 71–86.
Beymer D, Poggio T. Face recognition from one example view. In Proceedings of IEEE International Conference on Computer Vision. 1995 Jun 20; 500–507.
Barron I, Yeh HJ, Dinesh K, Sharma G. Dual modulated QR codes for proximal privacy and security. IEEE Trans Image Process. 2020 Nov 23; 30: 657–69.
Wu P, Liu J. Learning causal temporal relation and feature discrimination for anomaly detection. IEEE Trans Image Process. 2021 Mar 3; 30: 3513–27.
Wu YH, Gao SH, Mei J, Xu J, Fan DP, Zhang RG, Cheng MM. Jcs: An explainable covid-19 diagnosis system by joint classification and segmentation. IEEE Trans Image Process. 2021 Feb 18; 30: 3113–26.
Wang Y, Yang J, Wang L, Ying X, Wu T, An W, Guo Y. Light field image super-resolution using deformable convolution. IEEE Trans Image Process. 2020 Dec 8; 30: 1057–71.
Sahu A, Chowdhury AS. Together recognizing, localizing and summarizing actions in egocentric videos. IEEE Trans Image Process. 2021 Apr 8; 30: 4330–40.
Gu C, Lu X, He Y, Zhang C. Blur removal via blurred-noisy image pair. IEEE Trans Image Process. 2020 Nov 13; 30: 345–59.
Finlayson GD, Zhu Y. Designing color filters that make cameras more colorimetric. IEEE Trans Image Process. 2020 Nov 23; 30: 853–67.
Li YH, Lo IC, Chen HH. Deep face rectification for 360 dual-fisheye cameras. IEEE Trans Image Process. 2020 Sep 1; 30: 264–76.
Guo Y, Cai L, Zhang J. 3D face from X: Learning face shape from diverse sources. IEEE Trans Image Process. 2021 Mar 18; 30: 3815–27.
Dong C, Loy CC, He K, Tang X. Learning a deep convolutional network for image super-resolution. InComputer Vision -ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV 13 2014 (pp. 184–199). Springer International Publishing.
Yang H, Gong C, Huang K, Song K, Yin Z. Weighted feature histogram of multi-scale local patch using multi-bit binary descriptor for face recognition. IEEE Trans Image Process. 2021 Mar 18; 30: 3858–71.
Kasar MM, Bhattacharyya D, Kim TH. Face recognition using neural network: a review. Int J Secur Its Appl. 2016 Mar 1; 10(3): 81–100. 18. Bee Lim, Sanghyun S, Heewon K, Seungjun N, Mu LK. Enhanced Deep Residual Networks for Single Image Super-Resolution. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2017; 1132–1140.
