结合小波与递归神经网络的低分辨率人脸识别

doi:10.19665/j.issn1001-2400.2019.06.014

Abstract

Abstract:

To improve the accuracy of low-resolution face recognition with limited information, a method based on the Haar wavelet and recurrent neural network is proposed. First, the wavelet coefficients are directly predicted through the deep neural network. High-resolution face images with high-frequency information can be reconstructed by the inverse wavelet transform. Second, a recursive module is added to the convolutional neural network to increase the depth of the network, which can reduce the redundancy of parameters effectively. Finally, a fusion loss method is utilized, in which the loss of wavelet coefficients reconstruction and the perceptual are weighted and fusioned to generate images for recognition. Based on open dataset, the image reconstruction quality and recognition performance are compared, respectively. Experimental results show that sharper face images can be reconstructed even with extremely low resolutions (8×8, 16×16), and that its recognition ability outperforms that of state-of-the-art face super resolution algorithms.

Key words: Haar wavelet, recursive neural network, face recognition, fusion loss, super resolution

CLC Number:

TP183

OUYANG Ning,WANG Xian’ao,CAI Xiaodong,LIN Leping. Low resolution face recognition method based on wavelet and recursive neural networks[J].Journal of Xidian University, 2019, 46(6): 95-101.

Figures/Tables 10

卷积模块	SphereFace^[7]
Conv1.x	$3 × 3,64 × 1, S 2 3 × 3,64 3 × 3,64 × 1$
Conv2.x	$3 × 3,128 × 1, S 2 3 × 3,128 3 × 3,128 × 2$
Conv3.x	$3 × 3,256 × 1, S 2 3 × 3,256 3 × 3,256 × 4$
Conv4.x	$3 × 3,512 × 1, S 2 3 × 3,512 3 × 3,512 × 1$
Fc	512

References 17

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方法	Bicubic	SRCNN^[1]	DRRN^[5]	Wavelet-SRNet^[6]	文中方法
PSNR/dB	21.02	21.63	22.83	24.12	22.40
SSIM	0.577 1	0.624 8	0.706 9	0.778 8	0.704 0

方法	Bicubic	SRCNN^[1]	DRRN^[5]	Wavelet-SRNet^[6]	文中方法
PSNR/dB	25.25	26.12	27.94	29.23	26.26
SSIM	0.783 5	0.825 1	0.879 2	0.909 5	0.863 9

方法	Bicubic	SRCNN^[1]	DRRN^[5]	Wavelet-SRNet^[6]	文中方法
8×8(放大8倍)	64.38	62.81	79.40	87.65	90.18
16×16(放大4倍)	90.21	91.31	93.86	96.15	96.26

Low resolution face recognition method based on wavelet and recursive neural networks

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