LEFV: A Lightweight and Efficient System for Face Verification with Deep Convolution Neural Networks
Pages 222 - 227
Abstract
The emergence of deep learning has made great progress in face recognition. With the popularization of embedded devices, deploying the deep model on embedded devices has become a trend. Most high-precision models require lots of computation costs. Therefore, developing a lightweight deep face recognition system running on embedded devices is a hot topic in current research. To achieve high-accuracy real-time performance of an embedded device, we now present a simple and effective face recognition system LEFV, including face detection, face normalization and face recognition. The quantitative experiments on two large-scale challenging datasets, WIDER FACE dataset and IJB-A dataset, show competitive performances on both runtime and accuracy.
References
[1]
Simonyan, Karen, and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556(2014).
[2]
Hu, Peiyun, and Deva Ramanan. Finding tiny faces. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. DOI= https://doi.org/10.1109/CVPR.2017.166
[3]
Zhao, Jian, et al. Multi-Prototype Networks for Unconstrained Set-based Face Recognition. arXiv preprint arXiv:1902.04755 (2019).
[4]
Huang, Rui, et al. Beyond face rotation: Global and local perception gan for photorealistic and identity preserving frontal view synthesis. Proceedings of the IEEE International Conference on Computer Vision. 2017. DOI= https://doi.org/10.1109/ICCV.2017.267
[5]
Zhang, Shifeng, et al. S3FD: Single shot scale-invariant face detector. Proceedings of the IEEE International Conference on Computer Vision. 2017. DOI= https://doi.org/10.1109/ICCV.2017.30
[6]
Zhao, Jian, et al. "3d-aided dual-agent gans for unconstrained face recognition." IEEE transactions on pattern analysis and machine intelligence (2018). DOI= https://doi.org/10.1109/TPAMI.2018.285881
[7]
Najibi, Mahyar, et al. SSH: Single stage headless face detector. Proceedings of the IEEE International Conference on Computer Vision. 2017.DOI= https://doi.org/10.1109/ICCV.2017.522
[8]
Schroff, Florian, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. DOI= https://doi.org/10.1109/CVPR.2015.7298682
[9]
Iandola, Forrest, et al. Densenet: Implementing efficient convnet descriptor pyramids.arXiv preprint arXiv:1404.1869(2014).
[10]
Howard, Andrew G., et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications.arXiv preprint arXiv:1704.04861(2017).
[11]
Yang, Jiaolong, et al. Neural aggregation network for video face recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. DOI= https://doi.org/10.1109/CVPR.2017.554
[12]
Shu, Zhixin, et al. Neural face editing with intrinsic image disentangling. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. DOI= https://doi.org/10.1109/CVPR.2017.578
[13]
Iandola, Forrest N., et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size.arXiv preprint arXiv:1602.07360(2016).
[14]
Chollet, François. Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. DOI= https://doi.org/10.1109/CVPR.2017.195
[15]
Sun, Yi, Xiaogang Wang, and Xiaoou Tang. Deep learning face representation from predicting 10,000 classes. Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. DOI= https://doi.org/10.1109/CVPR.2014.244
[16]
Chen, Xi, et al. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. Advances in neural information processing systems. 2016.
[17]
Yang, Shuo, et al. Wider face: A face detection benchmark. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. DOI= https://doi.org/10.1109/CVPR.2016.596
[18]
Zhang, Kaipeng, et al. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters 23.10 (2016): 1499--1503. DOI= https://doi.org/10.1109/LSP.2016.2603342
[19]
Shrivastava, Ashish, et al. Learning from simulated and unsupervised images through adversarial training. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. DOI= https://doi.org/10.1109/CVPR.2017.241
[20]
Zhang, Xiangyu, et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. DOI= https://doi.org/10.1109/CVPR.2018.00716
[21]
Sun, Yi, Xiaogang Wang, and Xiaoou Tang. Deeply learned face representations are sparse, selective, and robust. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. DOI= https://doi.org/10.1109/CVPR.2015.7298907
[22]
Klare, Brendan F., et al. Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. DOI= https://doi.org/10.1109/CVPR.2015.7298803
[23]
Chen, Sheng, et al. Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices. arXiv preprint arXiv:1804.07573
[24]
Yin, Xi, et al. Towards large-pose face frontalization in the wild. Proceedings of the IEEE International Conference on Computer Vision. 2017. DOI= https://doi.org/10.1109/ICCV.2017.430
Index Terms
- LEFV: A Lightweight and Efficient System for Face Verification with Deep Convolution Neural Networks
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Published In
December 2019
270 pages
ISBN:9781450376822
DOI:10.1145/3376067
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In-Cooperation
- Shanghai Jiao Tong University: Shanghai Jiao Tong University
- Xidian University
- TU: Tianjin University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 February 2020
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ICVIP 2019
ICVIP 2019: 2019 the 3rd International Conference on Video and Image Processing
December 20 - 23, 2019
Shanghai, China
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