[1] Wang Y H, Zhu Y, Tan T N. Biometrics personal identification based on iris pattern[J]. Acta Automatica Sinica, 28, 1-10(2002).

[2] Xing L, Shi P F. A quality evaluation method of iris images[J]. Chinese Journal of Stereology and Image Analysis, 8, 108-113(2003).

[3] Yan M J, Wang Y J. Principle of iris computer recognition[J]. Progress in Biochemistry and Biophysics, 27, 348-350(2000).

[4] Thomas V, Chawla N V, Bowyer K W et al. Learning to predict gender from iris images[C]. //2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, September 27-29, 2007, Crystal City, VA, USA, 9871360(2007).

[5] Singh M, Nagpal S, Vatsa M et al. Gender and ethnicity classification of iris images using deep class-encoder[C]. //2017 IEEE International Joint Conference on Biometrics (IJCB), October 1-4, 2017, Denver, CO, USA., 666-673(2017).

[6] Bobeldyk D, Ross A. Analyzing covariate influence on gender and race prediction from near-infrared ocular images[J]. IEEE Access, 7, 7905-7919(2019).

[7] Tapia J, Aravena C. Gender classification from NIR iris images using deep learning[M]. //Bhanu B, Kumar A. Deep learning for biometrics. Advances in computer vision and pattern recognition, 84, 219-239(2017).

[8] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2020-08-04]. https://arxiv.org/abs/1409.1556

[9] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C]. //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 15523970(2015).

[10] LeCun Y, Boser B, Denker J S et al. Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1, 541-551(1989).

[11] Wang M, Fan T F, Yun W G et al. PFWG improved CNN multispectra remote sensing image classification[J]. Laser & Optoelectronics Progress, 56, 031003(2019).

[12] Hu L, Shan R, Wang F et al. Hyperspectral image classification based on dual-channel dilated convolution neural network[J]. Laser & Optoelectronics Progress, 57, 122803(2020).

[13] Wang Y N, Zhu D N, Wang H Q et al. Multispectral image classification of mural pigments based on convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 221001(2019).

[14] Liu Y Z, Jiang Z Q, Ma F et al. Hyperspectral image classification based on hypergraph and convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 111007(2019).

[15] Meng T, Liu Y H, Zhang K Y. Algorithm for pathological image diagnosis based on boosting convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 081001(2019).

[16] Wu Z F, Shen C H, van den Hengel A. Wider or deeper: revisiting the ResNet model for visual recognition[J]. Pattern Recognition, 90, 119-133(2019).

[17] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[EB/OL]. (2015-03-02)[2020-08-04]. https://arxiv.org/abs/1502.03167

[18] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

[19] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359(2010).

[20] Daugman J. How iris recognition works[J]. IEEE Transactions on Circuits and Systems for Video Technology, 14, 21-30(2004).