Paulo Renato C. Mendes
Antonio José G. Busson
Sérgio Colcher
Álan Lívio V. Guedes
ABSTRACT
Face recognition systems are present in many modern solutions and thousands of applications in our daily lives. However, current solutions are not easily scalable, especially when it comes to the addition of new targeted people. We propose a cluster-matching-based approach for face recognition in video. In our approach, we use unsupervised learning to cluster the faces present in both the dataset and targeted videos selected for face recognition. Moreover, we design a cluster matching heuristic to associate clusters in both sets that is also capable of identifying when a face belongs to a non-registered person. Our method has achieved a recall of 99.435% and a precision of 99.131% in the task of video face recognition. Besides performing face recognition, it can also be used to determine the video segments where each person is present.
- Y. Adini, Y. Moses, and S. Ullman. 1997. Face recognition: the problem of compensating for changes in illumination direction. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), 721--732.Google Scholar
Digital Library
- Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski. 2018. Linear algebraic structure of word senses, with applications to polysemy. Transactions of the Association for Computational Linguistics 6 (2018), 483--495.Google ScholarCross Ref
- Qiong Cao, Li Shen, Weidi Xie, Omkar M Parkhi, and Andrew Zisserman. 2018. Vggface2: A dataset for recognising faces across pose and age. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). IEEE, 67--74.Google ScholarDigital Library
- Harihara Santosh Dadi and GK Mohan Pillutla. 2016. Improved face recognition rate using HOG features and SVM classifier. IOSR Journal of Electronics and Communication Engineering 11, 04 (2016), 34--44.Google ScholarCross Ref
- Brendan J Frey and Delbert Dueck. 2007. Clustering by passing messages between data points. science 315, 5814 (2007), 972--976.Google Scholar
- V. Ghosal, P. Tikmani, and P. Gupta. 2009. Face Classification Using Gabor Wavelets and Random Forest. In 2009 Canadian Conference on Computer and Robot Vision. 68--73.Google Scholar
- Patrick Grother, Patrick Grother, Mei Ngan, Kayee Hanaoka, Chris Boehnen, and Lars Ericson. 2017. The 2017 IARPA Face Recognition Prize Challenge (FRPC). US Department of Commerce, National Institute of Standards and Technology.Google Scholar
- Kim Hazelwood, Sarah Bird, David Brooks, Soumith Chintala, Utku Diril, Dmytro Dzhulgakov, Mohamed Fawzy, Bill Jia, Yangqing Jia, Aditya Kalro, et al. 2018. Applied machine learning at facebook: A datacenter infrastructure perspective. In 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA). IEEE, 620--629.Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.Google ScholarCross Ref
- Julia Bell Hirschberg and Andrew Rosenberg. 2007. V-Measure: a conditional entropy-based external cluster evaluation. (2007).Google Scholar
- Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7132--7141.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
- Robert Layton, Paul Watters, and Richard Dazeley. 2013. Automated unsupervised authorship analysis using evidence accumulation clustering. Natural Language Engineering 19, 1 (2013), 95--120.Google ScholarCross Ref
- Stuart Lloyd. 1982. Least squares quantization in PCM. IEEE transactions on information theory 28, 2 (1982), 129--137.Google ScholarDigital Library
- I. Masi, Y. Wu, T. Hassner, and P. Natarajan. 2018. Deep Face Recognition: A Survey. In 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). 471--478.Google Scholar
- Iacopo Masi, Yue Wu, Tal Hassner, and Prem Natarajan. 2018. Deep face recognition: A survey. In 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, 471--478.Google ScholarCross Ref
- Rafael Pena, Felipe Ferreira, Frederico Caroli, Luiz Schirmer, and Hélio Lopes. 2020. Globo Face Stream: A System for Video Meta-data Generation in an Entertainment Industry Setting. 23 International Conference on Enterprise Information Systems (ICEIS) (2020).Google ScholarCross Ref
- Yongming Rao, Jiwen Lu, and Jie Zhou. 2017. Attention-aware deep reinforcement learning for video face recognition. In Proceedings of the IEEE international conference on computer vision. 3931--3940.Google ScholarCross Ref
- André Lira Rolim and Ed Porto Bezerra. 2008. Um Sistema de Identificação Automática de Faces Para Um Ambiente Virtual de Ensino e Aprendizagem. In Companion Proceedings of the XIV Brazilian Symposium on Multimedia and the Web (Vila Velha, Espírito Santo, Brazil) (WebMedia '08). Association for Computing Machinery, New York, NY, USA, 129--132. https://doi.org/10.1145/1809980.1810015Google ScholarDigital Library
- Edith M Ross and Florian Markowetz. 2016. OncoNEM: inferring tumor evolution from single-cell sequencing data. Genome biology 17, 1 (2016), 1--14.Google Scholar
- Peter J Rousseeuw. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics 20 (1987), 53--65.Google ScholarDigital Library
- 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
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Kihyuk Sohn, Sifei Liu, Guangyu Zhong, Xiang Yu, Ming-Hsuan Yang, and Manmohan Chandraker. 2017. Unsupervised domain adaptation for face recognition in unlabeled videos. In Proceedings of the IEEE International Conference on Computer Vision. 3210--3218.Google ScholarCross Ref
- Yi Sun, Xiaogang Wang, and Xiaoou Tang. 2014. Deep learning face representation from predicting 10,000 classes. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1891--1898.Google ScholarDigital Library
- Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, and Lior Wolf. 2014. Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1701--1708.Google ScholarDigital Library
- CJ Van Rijsbergen. 1979. Information retrieval 2nd edition butterworths. London available on internet (1979).Google ScholarDigital Library
- Joe H Ward Jr. 1963. Hierarchical grouping to optimize an objective function. Journal of the American statistical association 58, 301 (1963), 236--244.Google ScholarCross Ref
- Cameron Whitelam, Emma Taborsky, Austin Blanton, Brianna Maze, Jocelyn Adams, Tim Miller, Nathan Kalka, Anil K Jain, James A Duncan, Kristen Allen, et al. 2017. Iarpa janus benchmark-b face dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 90--98.Google ScholarCross Ref
- Jiaolong Yang, Peiran Ren, Dongqing Zhang, Dong Chen, Fang Wen, Hongdong Li, and Gang Hua. 2017. Neural aggregation network for video face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4362--4371.Google ScholarCross Ref
- 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 (Oct 2016), 1499--1503. https://doi.org/10.1109/LSP.2016.2603342Google ScholarCross Ref
Index Terms
- A Cluster-Matching-Based Method for Video Face Recognition
Recommendations
Online learning from local features for video-based face recognition
This paper presents an online learning approach to video-based face recognition that does not make any assumptions about the pose, expressions or prior localization of facial landmarks. Learning is performed online while the subject is imaged and gives ...
3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms
AbstractThe face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination ...
Age-Invariant Face Recognition
One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that ...
Comments