Research on Face Recognition Method based on Deep Learning

Hongrong Jing ； Hongjie Zhang ； Tiantian Chen ； Lu Chen ； Qian Hu

《International Core Journal of Engineering》 7卷7期 (2021/07) Pp. 165-170

https://doi.org/10.6919/ICJE.202107_7(7).0024

摘要

Face recognition is one of the important applications of computer vision, which plays an important role in production and life. The first half of this article explains the traditional face recognition algorithms, focusing on the PCA and LDA algorithms based on dimensionality reduction and the hand-designed LBP operator and HOG features. The basic principles are briefly outlined and their advantages and disadvantages are explained. Convolutional neural networks are widely used in face recognition. The second half is mainly based on deep learning algorithms. Several types of classic face recognition algorithms are summarized. For the first time, the DeepID algorithm using convolutional neural networks is proposed and used for the first time. The FaceNet algorithm with the concept of triples, the well-known residual network reduces network parameters. These algorithms have improved the accuracy of face recognition to varying degrees.

關鍵字

Face Recognition ； Convolutional Neural Network ； VGG ； DeepID FaceNet ； Residual

參考文獻

Li Gang, Gao Zheng. Overview of automatic face recognition methods [J]. Computer Application Research, 2003, 20(8):4-9,40. DOI:10.3969/j.issn.1001-3695.2003.08.002.

Ojala T, Pietikanen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texturee classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.

Zhang G, Huang X, Li SZ, Wang Y, Wu X. Boosting local binary pattern (LBP)-Based face recognition. Chinese Conference on Advances in Biometric Person Authentication, 2004, 3338: 179-186.

Yang B, Chen S. A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing, 2013, 120(10): 365-379

Zhou H, Lin D J, Huang T S. Static Hand Gesture Recognition based on Local Orientation Histogram Feature Distribution Model [C]// Computer Vision and Pattern Recognition Workshop, 2004: 161.