ABSTRACT
Unconstrained low-resolution (LR) face recognition is still a challenging problem in computer vision. In real-world scenarios, the gallery images are generally of high-resolution (HR), while the probe images may be of low resolution. In surveillance applications, the challenge of matching LR to HR is more common because the probe images are captured in low resolution while gallery images are of high resolution. LR to HR face matching is challenging because, in the embedding space, there is a need for a common subspace for mapping the LR and HR embeddings. In LR to LR face matching, where probe and gallery both belong to low-resolution, face identification is more difficult because very less visual information is present in the images. In LR to LR face matching, the challenge becomes very hard if faces are tiny in size and belong to low-resolution. In this paper, we implement a deep learning pipeline for matching the LR to HR and LR to LR faces. Due to the absence of LR and HR images of the same identity in the real-world datasets, we have also generated the LR images from HR images using the synthetic data approach. Extensive experimental analyses have been made to compare the performance to other state-of-the-art models.
- Jie Chang, Zhonghao Lan, Changmao Cheng, and Yichen Wei. 2020. Data uncertainty learning in face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5710–5719.Google ScholarCross Ref
- Sheng Chen, Yang Liu, Xiang Gao, and Zhen Han. 2018. MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices. https://doi.org/10.48550/ARXIV.1804.07573Google ScholarCross Ref
- Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR abs/1512.01274(2015). arXiv:1512.01274http://arxiv.org/abs/1512.01274Google Scholar
- Zhiyi Cheng, Xiatian Zhu, and Shaogang Gong. 2018. Low-Resolution Face Recognition. (2018).Google Scholar
- Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. 2019. ArcFace: Additive Angular Margin Loss for Deep Face Recognition. arxiv:1801.07698 [cs.CV]Google Scholar
- Changxing Ding and Dacheng Tao. 2017. Trunk-branch ensemble convolutional neural networks for video-based face recognition. IEEE transactions on pattern analysis and machine intelligence 40, 4(2017), 1002–1014.Google Scholar
- Sixue Gong, Yichun Shi, and Anil K. Jain. 2019. Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN). https://doi.org/10.48550/ARXIV.1902.07327Google ScholarCross Ref
- Mislav Grgic, Kresimir Delac, and Sonja Grgic. 2011. SCface–surveillance cameras face database. Multimedia tools and applications 51, 3 (2011), 863–879.Google Scholar
- Jianzhu Guo, Xiangyu Zhu, Chenxu Zhao, Dong Cao, Zhen Lei, and Stan Z Li. 2020. Learning meta face recognition in unseen domains. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6163–6172.Google ScholarCross Ref
- Peiyun Hu and Deva Ramanan. 2017. Finding tiny faces. In Proceedings of the IEEE conference on computer vision and pattern recognition. 951–959.Google ScholarCross Ref
- Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. 2007. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Technical Report 07-49. University of Massachusetts, Amherst.Google Scholar
- Hemank Lamba, Ankit Sarkar, Mayank Vatsa, Richa Singh, and Afzel Noore. 2011. Face recognition for look-alikes: A preliminary study. In 2011 International Joint Conference on Biometrics (IJCB). IEEE, 1–6.Google ScholarDigital Library
- W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song. 2017. SphereFace: Deep Hypersphere Embedding for Face Recognition. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6738–6746. https://doi.org/10.1109/CVPR.2017.713Google ScholarCross Ref
- Ze Lu, Xudong Jiang, and Alex Kot. 2018. Deep coupled resnet for low-resolution face recognition. IEEE Signal Processing Letters 25, 4 (2018), 526–530.Google ScholarCross Ref
- Yoanna Martínez-Díaz, Heydi Méndez-Vázquez, Luis S Luevano, Leonardo Chang, and Miguel Gonzalez-Mendoza. 2021. Lightweight low-resolution face recognition for surveillance applications. In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 5421–5428.Google ScholarCross Ref
- Yoanna Martínez-Díaz, Heydi Méndez-Vázquez, Luis S. Luevano, Leonardo Chang, and Miguel Gonzalez-Mendoza. 2021. Lightweight Low-Resolution Face Recognition for Surveillance Applications. In 2020 25th International Conference on Pattern Recognition (ICPR). 5421–5428. https://doi.org/10.1109/ICPR48806.2021.9412280Google ScholarCross Ref
- Omkar M Parkhi, Andrea Vedaldi, and Andrew Zisserman. 2015. Deep face recognition. (2015).Google Scholar
- Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition. 815–823.Google ScholarCross Ref
- Yichun Shi and Anil Jain. 2019. Probabilistic Face Embeddings. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 6901–6910. https://doi.org/10.1109/ICCV.2019.00700Google ScholarCross Ref
- Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. https://doi.org/10.48550/ARXIV.1409.1556Google ScholarCross Ref
- Yi Sun, Yuheng Chen, Xiaogang Wang, and Xiaoou Tang. 2014. Deep Learning Face Representation by Joint Identification-Verification. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS’14). MIT Press, Cambridge, MA, USA, 1988–1996.Google ScholarDigital Library
- Yi Sun, Xiaogang Wang, and Xiaoou Tang. 2014. Deep learning face representation by joint identification-verification. arXiv preprint arXiv:1406.4773(2014).Google Scholar
- Yi Sun, Xiaogang Wang, and Xiaoou Tang. 2015. Deeply learned face representations are sparse, selective, and robust. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2892–2900.Google ScholarCross Ref
- Thomas Swearingen and Arun Ross. 2021. Lookalike Disambiguation: Improving Face Identification Performance at Top Ranks. In 2020 25th International Conference on Pattern Recognition (ICPR). 10508–10515. https://doi.org/10.1109/ICPR48806.2021.9412063Google ScholarCross Ref
- Antonio Torralba, Rob Fergus, and William T. Freeman. 2008. 80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 11(2008), 1958–1970. https://doi.org/10.1109/TPAMI.2008.128Google ScholarDigital Library
- Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. 2016. A discriminative feature learning approach for deep face recognition. In European conference on computer vision. Springer, 499–515.Google ScholarCross Ref
- Xiang Wu, Ran He, Zhenan Sun, and Tieniu Tan. 2018. A light CNN for deep face representation with noisy labels. IEEE Transactions on Information Forensics and Security 13, 11(2018), 2884–2896.Google ScholarCross Ref
- Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li. 2014. Learning face representation from scratch. arXiv preprint arXiv:1411.7923(2014).Google Scholar
- Xi Yin, Ying Tai, Yuge Huang, and Xiaoming Liu. 2020. Fan: Feature adaptation network for surveillance face recognition and normalization. In Proceedings of the Asian Conference on Computer Vision.Google Scholar
- Erfan Zangeneh, Mohammad Rahmati, and Yalda Mohsenzadeh. 2020. Low resolution face recognition using a two-branch deep convolutional neural network architecture. Expert Systems with Applications 139 (2020), 112854. https://doi.org/10.1016/j.eswa.2019.112854Google ScholarDigital Library
- K. Zhang, Z. Zhang, Z. Li, and Y. Qiao. 2016. Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks. IEEE Signal Processing Letters 23, 10 (2016), 1499–1503.Google ScholarCross Ref
- Jingxiao Zheng, Rajeev Ranjan, Ching-Hui Chen, Jun-Cheng Chen, Carlos D Castillo, and Rama Chellappa. 2020. An automatic system for unconstrained video-based face recognition. IEEE Transactions on Biometrics, Behavior, and Identity Science 2, 3(2020), 194–209.Google ScholarCross Ref
Index Terms
- Synthetic Data Approach for Unconstrained Low-Resolution Face Recognition in Surveillance Applications✱
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