Abstract
This paper addresses the problem of face retrieval on large datasets by proposing an efficient representation for face videos. In comparison to the classical face verification problem, face retrieval poses additional challenges originating from database size. First, a different characteristic of recognition performance is required because retrieval scenarios have only very few correct face samples embedded in a large amount of imposters. In addition, the large number of samples in the database requires fast matching techniques. In this contribution, we present a face retrieval system which addresses these challenges. The first step consists of a set of measures to reduce frame descriptor dimension which saves processing time while keeping recognition performance. Afterwards, a novel Pyramid Mean Representation (PMR) of face videos is presented which allows for fast and accurate queries on large databases. The key concept is a hierarchical data representation with increasing sparsity which is used for an iterative query evaluation in a coarse to fine manner. The effectiveness of the proposed system is evaluated on the currently largest and most challenging public dataset of unconstrained videos, the YouTube Faces Database. In addition to the official verification test protocol, we define a protocol for face retrieval using a leave-one-out strategy. The proposed system achieves the best performance in this protocol with less processing time than baseline methods.
Keywords
- Face Recognition
- Principle Component Analysis
- Recognition Performance
- Local Binary Pattern
- Gesture Recognition
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: Computer Vision and Pattern Recognition (2011)
Phillips, P.J., Grother, P., Micheals, R.: Evaluation Methods in Face Recognition. In: Handbook of Face Recognition, pp. 551–574 (2011)
Bolle, R.M., Connell, J.H., Pankanti, S., Ratha, N.K., Senior, A.W.: The relation between the ROC curve and the CMC. In: Workshop on Automatic Identification Advanced Technologies (2005)
Wu, Z., Ke, Q., Sun, J., Shum, H.-Y.: Scalable face image retrieval with identity-based quantization and multireference reranking. Pattern Analysis and Machine Intelligence 33, 1991–2001 (2011)
Smith, B.M., Zhu, S., Zhang, L.: Face image retrieval by shape manipulation. In: Computer Vision and Pattern Recognition (2011)
Chen, B., Chen, Y., Kuo, Y., Hsu, W.: Scalable face image retrieval using attribute-enhanced sparse codewords. IEEE Transactions on Multimedia 15, 1163–1173 (2013)
Huang, C., Zhu, S., Yu, K.: Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval (2011)
Zhao, M., Yagnik, J., Adam, H., Bau, D.: Large Scale Learning and Recognition Of Faces in Web Videos. In: IEEE Automatic Face & Gesture Recognition, pp. 1–7 (2008)
Berg, T., Belhumeur, P.N.: Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification. In: British Machine Vision Conference (2012)
Wolf, L., Levy, N.: The svm-minus similarity score for video face recognition. In: Computer Vision and Pattern Recognition (2013)
Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: International Conference on Computer Vision (2005)
Ahonen, T., Hadid, A., Pietikainen, M.: Face Description with Local Binary Patterns: Application to Face Recognition. Pattern Analysis and Machine Intelligence 28, 2037–2041 (2006)
Herrmann, C.: Extending a local matching face recognition approach to low-resolution video. In: Advanced Video and Signal Based Surveillance (2013)
Jenkins, R., Burton, A.: 100% Accuracy In Automatic Face Recognition. Science 319, 435–435 (2008)
Ortiz, E.G., Wright, A., Shah, M.: Face recognition in movie trailers via mean sequence sparse representation-based classification. In: Computer Vision and Pattern Recognition (2013)
Chen, S., Mau, S., Harandi, M.T., Sanderson, C., Bigdeli, A., Lovell, B.C.: Face Recognition from Still Images to Video Sequences: A Local-feature-based Framework. EURASIP Journal on Image and Video Processing (2011)
Smucker, M.D., Allan, J., Carterette, B.: A comparison of statistical significance tests for information retrieval evaluation. In: Conference on Information and Knowledge Management (2007)
Cui, Z., Li, W., Xu, D., Shan, S., Chen, X.: Fusing robust face region descriptors via multiple metric learning for face recognition in the wild. In: Computer Vision and Pattern Recognition (2013)
Li, H., Hua, G., Lin, Z., Brandt, J., Yang, J.: Probabilistic elastic matching for pose variant face verification. In: Computer Vision and Pattern Recognition (2013)
Mendez-Vazquez, H., Martinez-Diaz, Y., Chai, Z.: Volume structured ordinal features with background similarity measure for video face recognition. In: International Conference on Biometrics (2013)
Best-Rowden, L., Klare, B., Klontz, J., Jain, A.K.: Video-to-video face matching: Establishing a baseline for unconstrained face recognition. In: Biometrics: Theory, Applications and Systems (2013)
Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Zou, J., Ji, Q., Nagy, G.: A Comparative Study of Local Matching Approach for Face Recognition. IEEE Transactions on Image Processing 16, 2617–2628 (2007)
Jabid, T., Kabir, M.H., Chae, O.: Local directional pattern (LDP) for face recognition. In: 2010 Digest of Technical Papers International Conference on Consumer Electronics (ICCE), pp. 329–330. IEEE (2010)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Liao, W.-H.: Region description using extended local ternary patterns. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 1003–1006. IEEE (2010)
Froba, B., Ernst, A.: Face detection with the modified census transform. In: Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 91–96. IEEE (2004)
Yamaguchi, O., Fukui, K., Maeda, K.: Face Recognition Using Temporal Image Sequence. In: Automatic Face and Gesture Recognition (1998)
Cevikalp, H., Triggs, B.: Face recognition based on image sets. In: Computer Vision and Pattern Recognition (2010)
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Herrmann, C., Beyerer, J. (2014). Pyramid Mean Representation of Image Sequences for Fast Face Retrieval in Unconstrained Video Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_29
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DOI: https://doi.org/10.1007/978-3-319-14364-4_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
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