[1] Man J Y[J]. Realization principle and prospect analysis of biosignature recognition technology China Security & Protection, 2014, 72-75.

[2] [2] TaigmanY, YangM, RanzatoM, et al.DeepFace: closing the gap to human-level performance in face verification[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE Press, 2014: 1701- 1708.

[3] Sun Y, Wang X G, Tang X O. Deep learning face representation from predicting 10, 000 classes[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA., 1891-1898(2014).

[4] Sun Y, Wang X G. -06-18)[2019-11-01]. https: ∥arxiv., org/abs/1406, 4773(2014).

[5] [5] SchroffF, KalenichenkoD, PhilbinJ. FaceNet: a unified embedding for face recognition and clustering[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE Press, 2015: 815- 823.

[6] [6] Liu WY, Wen YD, Yu ZD, et al.SphereFace: deep hypersphere embedding for face recognition[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE Press, 2017: 6738- 6746.

[7] Zhang Q, Zhuo L, Li J F et al. Vehicle color recognition using multiple-layer feature representations of lightweight convolutional neural network[J]. Signal Processing, 147, 146-153(2018).

[8] Iandola F N, Han S, Moskewicz M W, <0.5 MB model size[EB/OL] et al. -02-01)[2019-11-01]. https:∥arxiv., org/abs/1602, 07360(2016).

[9] Si Q, Li F F, Chen Q. Face recognition algorithm based on deep learning and feature fusion[J]. Electronic Science and Technology, 33, 18-22(2020).

[10] Zhang T P, Tang Y Y, Fang B et al. Face recognition under varying illumination using gradient faces[J]. IEEE Transactions on Image Processing, 18, 2599-2606(2009).