Abstract
Despite the Deep Neural Network (DNN) has achieved a great success in image recognition, the resource needed by DNN applications is still too much in terms of both memory usage and computing time, which makes it barely possible to deploy a whole DNN system on resource-limited devices such as smartphones and small embedded systems. In this paper, we present a DNN model named EffectFace designed for higher storage and computation efficiency without compromising the accuracy.
EffectFace includes two sub-modules, EffectDet for face detection and EffectApp for face recognition. In EffectDet we use sparse and small-scale convolution cores (filters) to reduce the number of weights for less memory usage. In EffectApp, we use pruning and weights-sharing technology to further reduce weights. At the output stage of the network, we use a new loss function rather than the traditional Softmax function to acquire feature vectors of the input face images, which reduces the dimension of the output of the network from n to fixed 128 where n equals to the number of categories to classify. Experiments show that, compared with previous models, the amounts of weights of our EffectFace is dramatically decreased (less than 10% of previous models) without losing the accuracy of recognition.
J. Zhai—This work is supported by National key R&D program of China grants No.2017YFB0202202, No. 2017YFB0203201 and NO. 2017YFC0820100, and by NSFC grant No. 61732002 and No. 61202425.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, H., Lin, Z., Shen, X., et al.: A convolutional neural network cascade for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325–5334 (2015)
Farfade, S.S., Saberian, M.J., Li, L.J.: Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 643–650R. ACM (2015)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Mignon, A.: PCCA: a new approach for distance learning from sparse pairwise constraints. In: Computer Vision and Pattern Recognition, pp. 2666–2672. IEEE (2012)
Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings CVPR (2005)
Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? Metric learning approaches for face identification. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 498–505. IEEE (2009)
Hu, J., Lu, J., Tan, Y.P.: Discriminative deep metric learning for face verification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1875–1882 (2014)
Huang, C., Zhu, S., Yu, K.: Large-scale strongly supervised ensemble metric learning: U.S. Patent 8,873,844[P], 28 October 2014
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892–2900 (2015)
Taigman, Y., Yang, M., Ranzato, M.A., et al.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering, 815–823 (2015)
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. Fiber 56(4), 3–7 (2016)
Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural networks. In: Advances in Neural Information Processing Systems (2015)
Deng, J., Berg, A., Satheesh, S., et al.: Large scale visual recognition challenge (2012). 1. www.image-net.org/challenges/LSVRC/2012
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition, pp. 580–587. IEEE (2013)
Zhang, K., Zhang, Z., Li, Z., et al.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)
LeCun, Y., Boser, B., Denker, J.S., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
LeCun, Y., Denker, J.S., Solla, S.A., et al.: Optimal brain damage. NIPs 2, 598–605 (1989)
Hassibi, B., Stork, D.G.: Second order derivatives for network pruning: optimal brain surgeon. In: Advances in Neural Information Processing Systems, p. 164 (1993)
Han, S., Pool, J., Tran, J., et al.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems, pp. 1135–1143 (2015)
Weinberger, K., Dasgupta, A., Langford, J., et al.: Feature hashing for large scale multitask learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1113–1120. ACM (2009)
Shi, Q., Petterson, J., Dror, G., et al.: Hash kernels for structured data. J. Mach. Learn. Res. 10(Nov), 2615–2637 (2009)
Ganchev, K., Dredze, M.: Small statistical models by random feature mixing. In: Proceedings of the ACL08 HLT Workshop on Mobile Language Processing, pp. 19–20 (2008)
Vaillant, R., Monrocq, C., Cun, Y.L.: Original approach for the localization of objects in images. IEE Proc. Vis. Image Sig. Process. 141(4), 245–250 (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, W. et al. (2018). EffectFace: A Fast and Efficient Deep Neural Network Model for Face Recognition. In: Li, C., Wu, J. (eds) Advanced Computer Architecture. ACA 2018. Communications in Computer and Information Science, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-13-2423-9_10
Download citation
DOI: https://doi.org/10.1007/978-981-13-2423-9_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2422-2
Online ISBN: 978-981-13-2423-9
eBook Packages: Computer ScienceComputer Science (R0)
