Compressing and Accelerating Neural Network for Facial Point Localization
- 250 Downloads
State-of-the-art deep neural networks (DNNs) have greatly improved the accuracy of facial landmark localization. However, DNN models usually have a huge number of parameters which cause high memory cost and computational complexity. To address this issue, a novel method is proposed to compress and accelerate large DNN models while maintaining the performance. It includes three steps: (1) importance-based pruning: compared with traditional connection pruning, weight correlations are introduced to find and prune unimportant neurons or connections. (2) Product quantization: product quantization helps to enforce weights shared. With the same size codebook, product quantization can achieve higher compression rate than scalar quantization. (3) Network retraining: to reduce compression difficulty and performance degradation, the network is retrained iteratively after compressing one layer at a time. Besides, all pooling layers are removed and the strides of their neighbor convolutional layers are increased to accelerate the network simultaneously. The experimental results of compressing a VGG-like model demonstrate the effectiveness of our proposed method, which achieves 26 × compression and 4 × acceleration while the root mean squared error (RMSE) increases by just 3.6%.
KeywordsNetwork compression Network acceleration Pruning Product quantization Facial point localization
We would like to thank professor Tian Qi (the University of Texas at San Antonio) and Zhou Xi (Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences) for their suggestions to this project.
This work is supported by the National Natural Science Foundation of China (61572307).
Compliance with Ethical Standards
This article does not contain any studies with human participants or animals performed by any of the authors.
Conflict of Interest
The authors declare that they have no conflict of interest.
- 2.Carreira J, Agrawal P, Fragkiadaki K, et al. Human pose estimation with iterative error feedback. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 4733–4742.Google Scholar
- 3.Dhall A, Goecke R, Joshi J, Sikka K, Gedeon T. Emotion recognition in the wild challenge 2014: baseline, data and protocol. Proceedings of the 16th international conference on multimodal interaction; 2014. p. 461–466.Google Scholar
- 4.Taigman Y, Yang M, Ranzato MA, Wolf L. Web-scale training for face identification. Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 2746–2754.Google Scholar
- 5.Chen JC, Patel VM, Chellappa R. Unconstrained face verification using deep cnn features. 2016 IEEE Winter conference on applications of computer vision; 2016. p. 1–9.Google Scholar
- 6.Sun Y, Wang X, Tang X. Deep convolutional network cascade for facial point detection. Proceedings of the IEEE conference on computer vision and pattern recognition; 2013. p. 3476–3483.Google Scholar
- 7.Zhang Z, Luo P, Loy CC, Tang X. Facial landmark detection by deep multi-task learning. European conference on computer vision; 2014. p. 94–108.Google Scholar
- 8.Chen Y, Yang J, Qian J. Recurrent neural network for facial landmark detection. Neurocomputing. 2017:26–38.Google Scholar
- 9.Jegou H, Douze M, Schmid C. Product quantization for nearest neighbor search. IEEE Trans Pattern Anal Mach Intell. 2011:117–128.Google Scholar
- 10.Chellapilla K, Puri S, Simard P. High performance convolutional neural networks for document processing. Tenth international workshop on frontiers in handwriting recognition; 2006.Google Scholar
- 11.Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
- 12.Xiong X, De la Torre F. Supervised descent method and its applications to face alignment. Proceedings of the IEEE conference on computer vision and pattern recognition; 2013. p. 532– 539.Google Scholar
- 13.Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580.
- 14.Han S, Pool J, Tran J, Dally W. Learning both weights and connections for efficient neural network. Advances in neural information processing systems; 2015. p. 1135–1143.Google Scholar
- 15.Sun Y, Wang X, Tang X. 2015. Sparsifying neural network connections for face recognition. arXiv:1512.01891.
- 16.Han S, Mao H, Dally WJ. 2015. Deep compression: compressing deep neural network with pruning, trained quantization and huffman coding. arXiv:1510.00149.
- 17.Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. 2015. Rethinking the inception architecture for computer vision. arXiv:1512.00567.
- 18.Courbariaux M, Bengio Y. 2016. Binarynet: training deep neural networks with weights and activations constrained to + 1 or − 1. arXiv:1602.02830.
- 19.Denil M, Shakibi B, Dinh L, de Freitas N. Predicting parameters in deep learning. Advances in neural information processing systems; 2013. p. 2148–2156.Google Scholar
- 20.Scardapane S, Comminiello D, Hussain A, Uncini A. 2016. Group sparse regularization for deep neural networks. arXiv:1607.00485.
- 21.Sainath TN, Kingsbury B, Sindhwani V, Arisoy E, Ramabhadran B. Low-rank matrix factorization for deep neural network training with high-dimensional output targets. 2013 IEEE international conference on acoustics, speech and signal processing; 2013. p. 6655–6659.Google Scholar
- 22.Denton EL, Zaremba W, Bruna J, LeCun Y, Fergus R. Exploiting linear structure within convolutional networks for efficient evaluation. Advances in neural information processing systems; 2014. p. 1269–1277.Google Scholar
- 23.Gong Y, Liu L, Yang M, Bourdev L. 2014. Compressing deep convolutional networks using vector quantization. arXiv:1412.6115.
- 24.Han S, Liu X, Mao H, et al. 2016. EIE: efficient inference engine on compressed deep neural network. arXiv:1602.01528.
- 25.Appuswamy R, Nayak T, Arthur J, et al. 2016. Structured convolution matrices for energy-efficient deep learning[j]. arXiv:1606.02407.
- 26.Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, ..., Darrell T. Caffe: convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on multimedia; 2014. p. 675–678.Google Scholar
- 27.Belhumeur PN, Jacobs DW, Kriegman DJ, Kumar N. Localizing parts of faces using a consensus of exemplars. IEEE Trans Pattern Anal Mach Intell. 2013:2930–2940.Google Scholar
- 28.Zhu X, Ramanan D. Face detection, pose estimation, and landmark localization in the wild. Computer vision and pattern recognition (CVPR); 2012. p. 2879–2886.Google Scholar
- 29.Liang L, Xiao R, Wen F, Sun J. Face alignment via component-based discriminative search. European conference on computer vision; 2008. p. 72–85.Google Scholar
- 30.Sagonas C, Tzimiropoulos G, Zafeiriou S, Pantic M. A semi-automatic methodology for facial landmark annotation. Proceedings of the IEEE conference on computer vision and pattern recognition workshops; 2013. p. 896–903.Google Scholar
- 31.Zhang Z, Luo P, Loy CC, et al. Learning deep representation for face alignment with auxiliary attributes. IEEE Trans Pattern Anal Mach Intell. 2016:918–930.Google Scholar