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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 2-4/2018

01.07.2017

Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks

verfasst von: Jun-Cheng Chen, Rajeev Ranjan, Swami Sankaranarayanan, Amit Kumar, Ching-Hui Chen, Vishal M. Patel, Carlos D. Castillo, Rama Chellappa

Erschienen in: International Journal of Computer Vision | Ausgabe 2-4/2018

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Abstract

Over the last 5 years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. This has been made possible due to the availability of large annotated datasets, a better understanding of the non-linear mapping between input images and class labels as well as the affordability of GPUs. In this paper, we present the design details of a deep learning system for unconstrained face recognition, including modules for face detection, association, alignment and face verification. The quantitative performance evaluation is conducted using the IARPA Janus Benchmark A (IJB-A), the JANUS Challenge Set 2 (JANUS CS2), and the Labeled Faces in the Wild (LFW) dataset. The IJB-A dataset includes real-world unconstrained faces of 500 subjects with significant pose and illumination variations which are much harder than the LFW and Youtube Face datasets. JANUS CS2 is the extended version of IJB-A which contains not only all the images/frames of IJB-A but also includes the original videos. Some open issues regarding DCNNs for face verification problems are then discussed.
Vorheriger Artikel Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit Detection
Nächster Artikel Subjects and Their Objects: Localizing Interactees for a Person-Centric View of Importance

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Fußnoten
1
While this paper was under review, several recent works have also reported improved numbers on the IJB-A dataset (Ranjan et al. 2016b) and its successive version Janus Challenge Set 3 (CS3) (Bodla et al. 2017). We refer the interested readers to these works for more details.
 
3
The JANUS CS2 dataset is not publicly available yet.
 
Literatur
AbdAlmageed, W., Wu, Y., Rawls, S., Harel, S., Hassne, T., Masi, I., et al. (2016). Face recognition using deep multi-pose representations. In IEEE winter conference on applications of computer vision (WACV).
Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037–2041.CrossRefMATH
Ahuja, R., Magnanti, T., & Orlin, J. (1993). Network flows: Theory, algorithms, and applications. Englewood Cliffs: Prentice Hall.MATH
Asthana, A., Zafeiriou, S., Cheng, S., & Pantic, M. (2013). Robust discriminative response map fitting with constrained local models. In IEEE conference on computer vision and pattern recognition (pp. 3444–3451).
Asthana, A., Zafeiriou, S., Cheng, S. Y., & Pantic, M. (2013). Robust discriminative response map fitting with constrained local models. In IEEE conference on computer vision and pattern recognition (pp. 3444–3451).
Babenko, B., Yang, M. H., & Belongie, S. (2009). Visual tracking with online multiple instance learning. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 983–990). IEEE.
Bae, S. H., & Yoon, K. J. (2014). Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In IEEE conference on computer vision and pattern recognition (CVPR).
Belhumeur, P. N., Jacobs, D. W., Kriegman, D. J., & Kumar, N. (2013). Localizing parts of faces using a consensus of exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(12), 2930–2940.CrossRef
Bodla, N., Zheng, J., Xu, H., Chen, J. C., Castillo, C. D., & Chellappa, R. (2017). Deep heterogeneous feature fusion for template-based face recognition. In IEEE winter conference on applications of computer vision (WACV).
Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., & Gool, L. V. (2009). Robust tracking-by-detection using a detector confidence particle filter. In IEEE international conference on computer vision (ICCV).
Bruna, J., & Mallat, S. (2013). Invariant scattering convolution networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1872–1886.CrossRef
Burgos-Artizzu, X. P., Perona, P., & Dollár, P. (2013). Robust face landmark estimation under occlusion. In IEEE international conference on computer vision, ICCV ’13 (pp. 1513–1520). Washington, DC: IEEE Computer Society. doi:10.​1109/​ICCV.​2013.​191.
Chen, D., Cao, X. D., Wang, L. W., Wen, F., & Sun, J. (2012a). Bayesian face revisited: A joint formulation. In European conference on computer vision (pp. 566–579).
Chen, D., Cao, X. D., Wen, F., & Sun, J. (2013). Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification. In IEEE conference on computer vision and pattern recognition.
Chen, Y. C., Patel, V. M., Phillips, P. J., & Chellappa, R. (2012b). Dictionary-based face recognition from video. In European conference on computer vision (ECCV).
Chen, J. C., Ranjan, R., Kumar, A., Chen, C. H., Patel, V. M., & Chellappa, R. (2015b). An end-to-end system for unconstrained face verification with deep convolutional neural networks. In IEEE international conference on computer vision workshop on ChaLearn looking at people (pp. 118–126).
Chen, D., Ren, S., Wei, Y., Cao, X., & Sun, J. (2014) Joint cascade face detection and alignment. In D. Fleet, T. Pajdla, B. Schiele & T. Tuytelaars (eds.) European conference on computer vision (Vol. 8694, pp. 109–122).
Chen, J. C., Sankaranarayanan, S., Patel, V. M., & Chellappa, R. (2015c). Unconstrained face verification using Fisher vectors computed from frontalized faces. In IEEE international conference on biometrics: Theory, applications and systems.CrossRef
Cheney, J., Klein, B., Jain, A. K., & Klare, B. F. (2015). Unconstrained face detection: State of the art baseline and challenges. In International conference on biometrics.
Comaschi, F., Stuijk, S., Basten, T., & Corporaal, H. (2015). Online multi-face detection and tracking using detector confidence and structured SVMs. In IEEE international conference on advanced video and signal based surveillance (AVSS).
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 681–685.CrossRef
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995). Active shape models-their training and application. Computer Vision and Image Understanding, 61(1), 38–59.CrossRef
Cristinacce, D., & Cootes, T. F. (2006). Feature detection and tracking with constrained local models. In British machine vision conference (Vol. 1, p. 3).
Crosswhite, N., Byrne, J., Parkhi, O. M., Stauffer, C., Cao, Q., & Zisserman, A. (2016). Template adaptation for face verification and identification. arXiv:​1603.​03958.
Davis, J. V., Kulis, B., Jain, P., Sra, S., & Dhillon, I. S. (2007). Information-theoretic metric learning. In International conference on machine learning (pp. 209–216).
Dollár, P., Welinder, P., & Perona, P. (2010). Cascaded pose regression. In IEEE conference on computer vision and pattern recognition (pp. 1078–1085). IEEE.
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., et al. (2013). Decaf: A deep convolutional activation feature for generic visual recognition. arXiv:​1310.​1531.
Du, M., & Chellappa, R. (2012). Face association across unconstrained video frames using conditional random fields. In European conference on computer vision (ECCV).
Duffner, S., & Odobez, J. (2013). Track creation and deletion framework for long-term online multiface tracking. IEEE Transactions on Image Processing, 22(1), 272–285.MathSciNetCrossRefMATH
Everingham, M., Gool, L. V., Williams, C. K. I., Winn, J., & Zisserman, A. (2009). The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2), 303–338.CrossRef
Farfade, S. S., Saberian, M. J., & Li, L. J. (2015). Multi-view face detection using deep convolutional neural networks. In International conference on multimedia retrieval.
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014a). Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE conference on computer vision and pattern recognition (pp. 580–587).
Girshick, R., Iandola, F., Darrell, T., & Malik, J. (2014b). Deformable part models are convolutional neural networks. In IEEE conference on computer vision and pattern recognition.
Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S. (2010). Multi-pie. Image and Vision Computing, 28(5), 807–813.CrossRef
Guillaumin, M., Verbeek, J., & Schmid, C. (2009) Is that you? Metric learning approaches for face identification. In IEEE international conference on computer vision (pp. 498–505).
Hassner, T., Harel, S., Paz, E., & Enbar, R. (2015). Effective face frontalization in unconstrained images. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 4295–4304).
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. arXiv:​1502.​01852.
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015). High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 583–596.CrossRef
Hu, J., Lu, J., & Tan, Y. P. (2014). Discriminative deep metric learning for face verification in the wild. In IEEE conference on computer vision and pattern recognition (pp. 1875–1882).
Huang, G. B., Mattar, M., Berg, T., & Learned-Miller, E. (2008a). Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In Workshop on faces in real-life images: detection, alignment, and recognition.
Huang, C., Wu, B., & Nevatia, R. (2008b). Robust object tracking by hierarchical association of detection responses. In European conference on computer vision (ECCV).
Jain, V., & Learned-Miller, E. (2010). Fddb: A benchmark for face detection in unconstrained settings. UM-CS-2010-009.
Kalal, Z., Mikolajczyk, K., & Matas, J. (2012). Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(7), 1409–1422.CrossRef
Kazemi, V., & Sullivan, J. (2014). One millisecond face alignment with an ensemble of regression trees. In IEEE conference on computer vision and pattern recognition (pp. 1867–1874).
Klare, B. F., Klein, B., Taborsky, E., Blanton, A., Cheney, J., Allen, K., et al. (2015). Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A. In IEEE conference on computer vision and pattern recognition.
Koestinger, M., Wohlhart, P., Roth, P. M., & Bischof, H. (2011). Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization. In First IEEE international workshop on benchmarking facial image analysis technologies.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).
Le, V., Brandt, J., Lin, Z., Bourdev, L., & Huang, T. S. (2012). Interactive facial feature localization. In European conference on computer vision (pp. 679–692). Springer.
Li, J., & Zhang, Y. (2013). Learning surf cascade for fast and accurate object detection. In 2013 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3468–3475). doi:10.​1109/​CVPR.​2013.​445.
Li, H., Hua, G., Lin, Z., Brandt, J., & Yang, J. (2013). Probabilistic elastic part model for unsupervised face detector adaptation. In IEEE international conference on computer vision (pp. 793–800).
Li, H., Lin, Z., Shen, X., Brandt, J., & Hua, G. (2015). A convolutional neural network cascade for face detection. In IEEE conference on computer vision and pattern recognition (pp. 5325–5334).
Liao, S., Jain, A. K., & Li, S. Z. (2016). A fast and accurate unconstrained face detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 211–223.CrossRef
Lui, Y. M., Beveridge, R. J., & Whitley, L. D. (2010). Adaptive appearance model and condensation algorithm for robust face tracking. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(3), 437–448.CrossRef
Masi, I., Tran, A. T., Leksut, J. T., Hassner, T., & Medioni, G. (2016). Do we really need to collect millions of faces for effective face recognition? arXiv:​1603.​07057.
Mathias, M., Benenson, R., Pedersoli, M., & Gool, L. V. (2014). Face detection without bells and whistles. In European conference on computer vision (Vol. 8692, pp. 720–735).
Mignon, A., & Jurie, F. (2012). Pcca: A new approach for distance learning from sparse pairwise constraints. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2666–2672).
Nguyen, H. V., & Bai, L. (2010). Cosine similarity metric learning for face verification. In Asian conference on computer vision (pp. 709–720). Springer.
Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep face recognition. In British machine vision conference.
Ranjan, R., Castillo, C. D., & Chellappa, R. (2017). L2-constrained softmax loss for discriminative face verification. arXiv:​1703.​09507.
Ranjan, R., Patel, V. M., & Chellappa, R. (2015). A deep pyramid deformable part model for face detection. In IEEE international conference on biometrics: Theory, applications and systems.CrossRef
Ranjan, R., Patel, V. M., & Chellappa, R. (2016a). HyperFace: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. arXiv:​1603.​01249.
Ranjan, R., Sankaranarayanan, S., Castillo, C. D., & Chellappa, R. (2016b). An all-in-one convolutional neural network for face analysis. arXiv:​1611.​00851.
Ren, S., Cao, X., Wei, Y., & Sun, J. (2014). Face alignment at 3000 fps via regressing local binary features. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1685–1692). doi:10.​1109/​CVPR.​2014.​218
Ross, G. (2015). Fast r-cnn. In IEEE international conference on computer vision (pp. 1440–1448).
Roth, M., Bauml, M., Nevatia, R., & Stiefelhagen, R. (2012). Robust multi-pose face tracking by multi-stage tracklet association. In International conference on pattern recognition (ICPR).
RoyChowdhury, A., Lin, T. Y., Maji, S., & Learned-Miller, E. (2016). One-to-many face recognition with bilinear cnns. In IEEE winter conference on applications of computer vision (WACV).
Sankaranarayanan, S., Alavi, A., Castillo, C. D., & Chellappa, R. (2016a). Triplet probabilistic embedding for face verification and clustering. In IEEE international conference on biometrics theory, applications and systems (BTAS) (pp. 1–8).
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. arXiv:​1503.​03832.
Shi, J., & Tomasi, C. (1994). Good features to track. In IEEE conference on computer vision and pattern recognition (CVPR).
Simonyan, K., Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2013). Fisher vector faces in the wild. In British machine vision conference (Vol. 1, p. 7).
Sun, Y., Wang, X., & Tang, X. (2013). Deep convolutional network cascade for facial point detection. In IEEE conference on computer vision and pattern recognition (pp. 3476–3483).
Sun, Y., Wang, X., & Tang, X. (2014). Deeply learned face representations are sparse, selective, and robust. arXiv:​1412.​1265.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2014). Going deeper with convolutions. arXiv:​1409.​4842.
Taigman, Y., Wolf, L., & Hassner, T. (2009). Multiple one-shots for utilizing class label information. In British machine vision conference (pp. 1–12).
Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In IEEE conference on computer vision and pattern recognition (pp. 1701–1708).
Tzimiropoulos, G., Pantic, M. (2014). Gauss–Newton deformable part models for face alignment in-the-wild. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1851–1858). doi:10.​1109/​CVPR.​2014.​239.
Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.CrossRef
Wang, P., & Ji, Q. (2008). Robust face tracking via collaboration of generic and specific models. IEEE Transactions on Image Processing, 17(7), 1189–1199.MathSciNetCrossRef
Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10, 207–244.MATH
Wolf, L., Hassner, T., & Taigman, Y. (2009). The one-shot similarity kernel. In International conference on computer vision (pp. 897–902). IEEE.
Xiong, X., & la Torre, F. D. (2013). Supervised descent method and its applications to face alignment. In 2013 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 532–539). doi:10.​1109/​CVPR.​2013.​75.
Xiong, L., Karlekar, J., Zhao, J., Feng, J., Pranata, S., & Shen, S. (2017). A good practice towards top performance of face recognition: Transferred deep feature fusion. arXiv:​1704.​00438.
Yang, S., Luo, P., Loy, C. C., & Tang, X. (2015a). From facial parts responses to face detection: A deep learning approach. In IEEE international conference on computer vision.
Yang, J., Ren, P., Chen, D., Wen, F., Li, H., & Hua, G. (2016). Neural aggregation network for video face recognition. arXiv:​1603.​05474.
Yang, J., Ren, P., Zhang, D., Chen, D., Wen, F., Li, H., et al. (2017). Neural aggregation network for video face recognition. In IEEE conference on computer vision and pattern recognition (CVPR).
Yang, B., Yan, J., Lei, Z., & Li, S. Z. (2015b). Convolutional channel features. In IEEE international conference on computer vision.
Yoon, J. H., Yang, M. H., Lim, J., & Yoon, K. J. (2015). Bayesian multi-object tracking using motion context from multiple objects. In IEEE winter conference on applications of computer vision (WACV).
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in neural information processing systems (NIPS) (pp. 3320–3328).
Zhang, J., Shan, S., Kan, M., & Chen, X. (2014). Coarse-to-fine auto-encoder networks (CFAN) for real-time face alignment. In European conference on computer vision ECCV (pp. 1–16). doi:10.​1007/​978-3-319-10605-2_​1.
Zhao, W. Y., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM Computing Surveys, 35(4), 399–458.CrossRef
Zhu, S., Li, C., Chen, C. L., & Tang, X. (2015). Face alignment by coarse-to-fine shape searching. In IEEE conference on computer vision and pattern recognition (CVPR).
Zhu, X., & Ramanan, D. (2012). Face detection, pose estimation, and landmark localization in the wild. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2879–2886). IEEE.
Metadaten
Titel
Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks
verfasst von
Jun-Cheng Chen
Rajeev Ranjan
Swami Sankaranarayanan
Amit Kumar
Ching-Hui Chen
Vishal M. Patel
Carlos D. Castillo
Rama Chellappa
Publikationsdatum
01.07.2017
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 2-4/2018
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-017-1029-3

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