Expression Recognition Based on Attention-Split Convolutional Residual Network

Jiamin Chen; Yang Xu

doi:10.3788/LOP202259.1815009

[1] Yin P B, Pan W M, Zhang H J. Lightweight facial expression recognition method based on convolutional attention[J]. Laser & Optoelectronics Progress, 58, 1210023(2021).

[2] Yao L S, Xu G M, Zhao F. Facial expression recognition based on local feature fusion of convolutional neural network[J]. Laser & Optoelectronics Progress, 57, 041513(2020).

[3] Yang X, Shang Z H. Facial expression recognition based on improved AlexNet[J]. Laser & Optoelectronics Progress, 57, 243-250(2020).

[4] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[5] Wu H H, Su H S, Liu G H et al. Facial expression recognition algorithm based on cosine distance loss function[J]. Laser & Optoelectronics Progress, 56, 241502(2019).

[6] Lin M, Chen Q, Yan S C. Network in network[EB/OL]. https://arxiv.org/abs/1312.4400

[7] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[8] Xie X K, Zhou Y, Kung S Y. Exploring highly efficient compact neural networks for image classification[C], 2930-2934(2020).

[9] Huang W L, Wang J J, Xin X M et al. Channel-modulated multibranch convolutional neural network[C], 1854-1859(2020).

[10] Xie S N, Girshick R, Dollár P et al. Aggregated residual transformations for deep neural networks[C], 5987-5995(2017).

[11] Chollet F. Xception: deep learning with depthwise separable convolutions[C], 1800-1807(2017).

[12] Howard A G, Zhu M, Chen B et al. MobileNets: efficient convolutional neural networks for mobile applications[EB/OL]. https://arxiv.org/abs/1704.04861

[13] Zhang X Y, Zhou X Y, Lin M X et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C], 6848-6856(2018).

[14] Chen Y P, Fan H Q, Xu B et al. Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution[C], 3434-3443(2019).

[15] Singh P, Verma V K, Rai P et al. HetConv: heterogeneous kernel-based convolutions for deep CNNs[C], 4830-4839(2019).

[16] Li X, Wang W H, Hu X L et al. Selective kernel networks[C], 510-519(2019).

[17] Han K, Wang Y, Tian Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020).

[18] Zhang Q L, Jiang Z Q, Lu Q S et al. Split to be slim: an overlooked redundancy in vanilla convolution[EB/OL]. https://arxiv.org/abs/2006.12085

[19] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021).

[20] Zhang T, Qi G J, Xiao B et al. Interleaved group convolutions[C], 4383-4392(2017).

[21] Miao S, Xu H Y, Han Z Q et al. Recognizing facial expressions using a shallow convolutional neural network[J]. IEEE Access, 7, 78000-78011(2019).

[22] Liang H G, Lei Y X. Expression recognition with separable convolution channel enhancement features[J]. Computer Engineering and Applications, 58, 184-192(2022).

[23] Zhou J C, Jia X, Shen L L et al. Improved softmax loss for deep learning-based face and expression recognition[J]. Cognitive Computation and Systems, 1, 97-102(2019).

[24] Li S, Deng W H. Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition[J]. IEEE Transactions on Image Processing, 28, 356-370(2019).

[25] Deng J, Pang G Y, Zhang Z Y et al. cGAN based facial expression recognition for human-robot interaction[J]. IEEE Access, 7, 9848-9859(2019).

[26] Koujan M R, Alharbawee L, Giannakakis G et al. Real-time facial expression recognition “in the wild” by disentangling 3D expression from identity[C], 24-31(2020).

[27] Zhou X Z, Jin K, Shang Y Y et al. Visually interpretable representation learning for depression recognition from facial images[J]. IEEE Transactions on Affective Computing, 11, 542-552(2020).