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Scalable Face Retrieval by Simple Classifiers and Voting Scheme

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Face and Facial Expression Recognition from Real World Videos (FFER 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8912))

Abstract

In this paper, we propose a scalable face retrieval method on large data. In order to search a particular person from videos on the Web, face recognition is one of the most effective methods. Needless to say that retrieving faces from videos are more challenging than that from a still image due to inconsistency in imaging conditions such as change of view point, lighting condition and resolution. However, dealing with them is not enough to realize the retrieval on large data. In addition, a face recognition method on the videos should be highly scalable as the number of the videos on the Web is enormous. Existing face recognition methods do not scale. In order to realize scalable face recognition, we propose a novel face recognition method. The proposed method is scalable even if the data is million-scale with high accuracy. The proposed method uses local features for face representation, and an approximate nearest neighbor (ANN) search for feature matching to reduce computational time. A voting scheme is used for recognition to compensate for low accuracy of the ANN search. We created a 5 million database and evaluated the proposed method. As results, the proposed method showed more than thousand times faster than a conventional sublinear method. Moreover, the proposed method recognized face images with a top 1000 cumulative accuracy of 100% in 139 ms recognition time (excluding preprocessing and feature extraction for the query image) per query image on the 5 million face database.

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References

  1. Belhumber, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transaction on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)

    Google Scholar 

  2. Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification. In: Proc. 2013 IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  3. Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)

    Google Scholar 

  4. Gao, W., Cao, B., Shan, S., Zhou, D., Zhang, X., Zhao, D., Al, S.: The CAS-PEAL large-scale Chinese face database and evaluation protocols. Joint Research & Development Laboratory, Technical report (2004)

    Google Scholar 

  5. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: Illumination cone models for face recognition under variable lightning and pose. IEEE Transaction on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)

    Article  Google Scholar 

  6. Grgic, M., Delac, K., Grgic, S.: Scface - surveillance cameras face database. In: Multiedia Tools and Applications, pp. 1–17 (2009)

    Google Scholar 

  7. Heisele, B., Ho, P., Poggio, T.: Face recognition with support vector machines: Global versus component-based approach. In: Proceedings on Eighth IEEE International Conference on Computer Vision, Vancouver, Canada, vol. 2, pp. 688–694 (2001)

    Google Scholar 

  8. Iwamura, M., Kunze, K., Kato, Y., Utsumi, Y., Kise, K.: Haven’t we met before? – a realistic memory assistance system to remind you of the person in front of you. In: Proc. 5th Augmented Human (AH 2014), March 2014

    Google Scholar 

  9. Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 506–513 (2004)

    Google Scholar 

  10. Kim, T., Stenger, B., Kittler, J., Cipolla, R.: Incremental linear discriminant analysis using sufficient spanning sets and its applications. International Journal of Computer Vision 91, 216–232 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  11. Kise, K., Noguchi, K., Iwamura, M.: Memory efficient recognition of specific objects with local features. In: Proceedings of the 19th International Conference of Pattern Recognition (2005)

    Google Scholar 

  12. Kise, K., Noguchi, K., Iwamura, M.: Robust and efficient recognition of low-quality images by cascaded recognizers with massive local features. In: Proceedings of the 1st International Workshop on Emergent Issues in Large Amount of Visual Data (WS-LAVD 2009), pp. 2125–2132 (2009)

    Google Scholar 

  13. Kozakaya, T., Yamaguchi, O.: Face recognition by projection-based 3D normalization and shading subspace orthogonalization. In: FGR 2006, pp. 163–168 (2006)

    Google Scholar 

  14. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)

    Google Scholar 

  15. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision. 60, 91–110 (2004)

    Article  Google Scholar 

  16. Martínez, A., Benavente, R.: The AR face database. Technical Report 24, Computer Vision Center, Barcelona (1998). http://www2.ece.ohio-state.edu/aleix/ARdatabase.html

  17. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transaction on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  18. Mita, T., kaneko, T., Stenger, B., Hori, O.: Discriminative feature co-occurrence selection for object detection. IEEE Transaction on Pattern Analysis and Machine Intelligence 30(7), 1257–1269 (2008)

    Google Scholar 

  19. Nefian, A.V., Khosravi, M., III, M.H.H.: Real-time human face detection from uncontrolled environments. In: SPIE Visual Communications on Image Processing (1997)

    Google Scholar 

  20. Nowak, E., Jurie, F., Triggs, B.: Sampling Strategies for Bag-of-Features Image Classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  21. Phillips, J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trasaction on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  22. Phillips, P.J.: Support vector machines applied to face recognition. Advances in Neural Information Processing Systems 11, 803–809 (1998)

    Google Scholar 

  23. Sato, T., Iwamura, M., Kise, K.: Fast and memory efficient approximate nearest neighbor search with distance estimation based on space indexing. Prmu 2012–142, IEICE Technical Report (February 2013)

    Google Scholar 

  24. Schwartz, W.R., Guo, H., Davis, L.S.: A Robust and Scalable Approach to Face Identification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 476–489. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  25. Sharma, A., Dubey, A., Tripathi, P., Kumar, V.: Pose invariant virtual classifiers from single training image using novel hybrid-eigenfaces. Neurocomputing 73, 1868–1880 (2010)

    Article  Google Scholar 

  26. Shi, Q., Li, H., Shen, C.: Rapid face recognition using hashing. In: Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2753–2760 (2010)

    Google Scholar 

  27. Turk, M., Pentland, A.: Eigenface for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)

    Article  Google Scholar 

  28. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transaction on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)

    Article  Google Scholar 

  29. Yang, J., Ahang, D., Frangi, A.F., yu Yang, J.: Two-dimensional pca: a new approach to appearance-based face representation and recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004)

    Google Scholar 

  30. Yang, M.H.: Kernel eigenfaces vs. kernel fisherfaces: Face recognition using kernel methods. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 215–220. Washington, DC, May 2002

    Google Scholar 

  31. Yuasa, M., Kozakaya, T., Yamaguchi, O.: An efficient 3D geometrical consistency criterion for detection of a set of facial feature points. IEICE - Trans. Inf. Syst. E91-D, 1871–1877 (2008)

    Google Scholar 

  32. Zhao, W., Chellappa, R., Krishnaswamy, A.: Discriminant analysis of principal components for face recognition. In: Proceedings on IEEE International Conference on Face and Gesture Recognition (FG1998), pp. 14–16. Nara, Japan, April 1998

    Google Scholar 

  33. Zhu, C.Z., Zhou, X., Satoh, S.: Bag-of-words against nearest-neighbor search for visual object retrieval. In: Proceedings of 2013 2nd IAPR Asian Conference on Pattern Recognition, pp. 626–630 (2013)

    Google Scholar 

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Authors and Affiliations

  1. Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuencho, Naka, Sakai, Osaka, 599-8531, Japan

    Yuzuko Utsumi, Yuji Sakano, Keisuke Maekawa, Masakazu Iwamura & Koichi Kise

Authors
  1. Yuzuko Utsumi
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  2. Yuji Sakano
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  3. Keisuke Maekawa
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  4. Masakazu Iwamura
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  5. Koichi Kise
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Corresponding author

Correspondence to Yuzuko Utsumi .

Editor information

Editors and Affiliations

  1. Computer and Systems Engineering, Pensselaer Polytechnic Institute, Troy, New York, USA

    Qiang Ji

  2. Department of Architecture, Design, and Media Technology, Aalborg, Denmark

    Thomas B. Moeslund

  3. Department of Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA

    Gang Hua

  4. Department of Architecture, Design, and Media Technology, Aalborg, Denmark

    Kamal Nasrollahi

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© 2015 Springer International Publishing Switzerland

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Utsumi, Y., Sakano, Y., Maekawa, K., Iwamura, M., Kise, K. (2015). Scalable Face Retrieval by Simple Classifiers and Voting Scheme. In: Ji, Q., B. Moeslund, T., Hua, G., Nasrollahi, K. (eds) Face and Facial Expression Recognition from Real World Videos. FFER 2014. Lecture Notes in Computer Science(), vol 8912. Springer, Cham. https://doi.org/10.1007/978-3-319-13737-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-13737-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13736-0

  • Online ISBN: 978-3-319-13737-7

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