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Face Image Retrieval Revisited

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Similarity Search and Applications (SISAP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9371))

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Abstract

The objective of face retrieval is to efficiently search an image database with detected faces and identify such faces that belong to the same person as a query face. Unlike most related papers, we concentrate on both retrieval effectiveness and efficiency. High retrieval effectiveness is achieved by proposing a new fusion approach which integrates existing state-of-the-art detection as well as matching methods. We further significantly improve a retrieval quality by employing the concept of multi-face queries along with optional relevance feedback. To be able to efficiently process queries on databases with millions of faces, we apply a specialized indexing algorithm. The proposed solutions are compared against four existing open-source and commercial technologies and experimentally evaluated on the standardized FERET dataset and on a real-life dataset of more than one million face images. The retrieval results demonstrate a significant gain in effectiveness and two-orders of magnitude more efficient query processing, with respect to a single technology executed sequentially.

P. Zezula—Supported by the national project No. VG20122015073.

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References

  1. Chan, C.H., Tahir, M.A., Kittler, J., Pietikäinen, M.: Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors. IEEE Trans. on Pattern Analysis and Mach. Int., 1164–1177 (2013)

    Google Scholar 

  2. Chen, B.C., Chen, Y.Y., Kuo, Y.H., Hsu, W.H.: Scalable face image retrieval using attribute-enhanced sparse codewords. IEEE Transactions on Multimedia 15(5), 1163–1173 (2013)

    Article  Google Scholar 

  3. Choi, W., Pantofaru, C., Savarese, S.: Detecting and tracking people using an rgb-d camera via multiple detector fusion. In: International Conference on Computer Vision Workshops, pp. 1076–1083 (2011)

    Google Scholar 

  4. Degtyarev, N., Seredin, O.: A geometric approach to face detector combining. In: Sansone, C., Kittler, J., Roli, F. (eds.) MCS 2011. LNCS, vol. 6713, pp. 299–308. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  5. Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. Journal of Computer and System Sciences 66(4), 614–656 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  6. Górecki, T.: Sequential combining in discriminant analysis. Journal of Applied Statistics, 398–408 (2015)

    Google Scholar 

  7. Hua, G., Yang, M.H., Learned-Miller, E., Ma, Y., Turk, M., Kriegman, D.J., Huang, T.S.: Introduction to the special section on real-world face recognition. IEEE Trans. on Pattern Analysis and Machine Int. 33(10), 1921–1924 (2011)

    Article  Google Scholar 

  8. Huang, Y., Liu, Q., Metaxas, D.N.: A component-based framework for generalized face alignment. IEEE Trans. on Systems, Man, and Cybernetics, 287–298 (2011)

    Google Scholar 

  9. Cech, J., Franc, V., Matas, J.: A 3d approach to facial landmarks: detection, refinement, and tracking. In: Int. Conf. on Pattern Recognition (ICPR 2014), p. 6 (2014)

    Google Scholar 

  10. Jafri, R., Arabnia, H.R.: A survey of face recognition techniques. Journal of Information Processing Systems, 41–68 (2009)

    Google Scholar 

  11. Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-color modeling and detection methods. Pattern Recognition, 1106–1122 (2007)

    Google Scholar 

  12. Klontz, J.C., Klare, B.F., Klum, S., Jain, A.K., Burge, M.J.: Open source biometric recognition. In: BTAS 2013, pp. 1–8 (2013)

    Google Scholar 

  13. Lai, J.H., Yuen, P.C., Feng, G.C.: Face recognition using holistic fourier invariant features. Pattern Recognition, 95–109 (2001)

    Google Scholar 

  14. Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond sliding windows: object localization by efficient subwindow search. In: International Conference on Computer Vision and Pattern Recognition (CVPR 2008), pp. 1–8 (2008)

    Google Scholar 

  15. Nanni, L., Lumini, A., Brahnam, S.: Likelihood ratio based features for a trained biometric score fusion. Expert Systems with Applications, 58–63 (2011)

    Google Scholar 

  16. Nanni, L., Lumini, A., Ferrara, M., Cappelli, R.: Combining biometric matchers by means of machine learning and statistical approaches. Neurocomputing, 526–535 (2015)

    Google Scholar 

  17. Naseem, I., Togneri, R., Bennamoun, M.: Linear regression for face recognition. IEEE Trans. on Pattern Analysis and Machine Int., 2106–2112 (2010)

    Google Scholar 

  18. Novak, D., Batko, M., Zezula, P.: Metric Index: An Efficient and Scalable Solution for Precise and Approximate Similarity Search. Inf. Sys. 36(4), 721–733 (2011)

    Article  Google Scholar 

  19. Segundo, M.P., Silva, L., Bellon, O.R.P., Queirolo, C.C.: Automatic face segmentation and facial landmark detection in range images. IEEE Transactions on Systems, Man, and Cybernetics, 1319–1330 (2010)

    Google Scholar 

  20. Park, U., Jain, A.K.: Face matching and retrieval using soft biometrics. IEEE Transactions on Information Forensics and Security, 406–415 (2010)

    Google Scholar 

  21. Sikora, T.: The mpeg-7 visual standard for content description-an overview. IEEE Transactions on Circuits and Systems for Video Technology 11(6), 696–702 (2001)

    Article  MathSciNet  Google Scholar 

  22. Subburaman, V.B., Marcel, S.: Alternative search techniques for face detection using location estimation and binary features. Computer Vision and Image Understanding, 551–570 (2013)

    Google Scholar 

  23. Tan, X., Chen, S., Zhou, Z.H., Zhang, F.: Face recognition from a single image per person: A survey. Pattern Recognition, 1725–1745 (2006)

    Google Scholar 

  24. Tsao, W.K., Lee, A.J.T., Liu, Y.H., Chang, T.W., Lin, H.H.: A data mining approach to face detection. Pattern Recognition, 1039–1049 (2010)

    Google Scholar 

  25. Uřičář, M., Franc, V., Hlaváč, V.: Detector of facial landmarks learned by the structured output SVM. In: Int. Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications. vol. 1, pp. 547–556. SciTePress (2012)

    Google Scholar 

  26. Wang, X., Han, T., Yan, S.: An hog-lbp human detector with partial occlusion handling. In: 12th International Conference on Computer Vision, pp. 32–39 (2009)

    Google Scholar 

  27. Wu, Z., Ke, Q., Sun, J., Shum, H.Y.: Scalable face image retrieval with identity-based quantization and multireference reranking. In: Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2010), pp. 3469–3476. IEEE (2010)

    Google Scholar 

  28. Yang, J., Liu, C., Zhang, L.: Color space normalization: Enhancing the discriminating power of color spaces for face recognition. Pattern Recognition, 1454–1466 (2010)

    Google Scholar 

  29. Yang, M.H., Kriegman, D., Ahuja, N.: Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34–58 (2002)

    Google Scholar 

  30. Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Advances in Database Systems, vol. 32. Springer (2006)

    Google Scholar 

  31. Zhang, L., Kalashnikov, D.V., Mehrotra, S.: A unified framework for context assisted face clustering. In: Int. Conf. on Multimedia Retrieval, pp. 9–16. ACM (2013)

    Google Scholar 

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Authors and Affiliations

  1. Masaryk University, Botanicka 68a, 602 00, Brno, Czech Republic

    Jan Sedmidubsky, Vladimir Mic & Pavel Zezula

Authors
  1. Jan Sedmidubsky
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  2. Vladimir Mic
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  3. Pavel Zezula
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Corresponding author

Correspondence to Jan Sedmidubsky .

Editor information

Editors and Affiliations

  1. ISTI-CNR, Pisa, Italy

    Giuseppe Amato

  2. University of Strathclyde, Glasgow, United Kingdom

    Richard Connor

  3. ISTI-CNR, Pisa, Italy

    Fabrizio Falchi

  4. ISTI-CNR, Pisa, Italy

    Claudio Gennaro

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Sedmidubsky, J., Mic, V., Zezula, P. (2015). Face Image Retrieval Revisited. In: Amato, G., Connor, R., Falchi, F., Gennaro, C. (eds) Similarity Search and Applications. SISAP 2015. Lecture Notes in Computer Science(), vol 9371. Springer, Cham. https://doi.org/10.1007/978-3-319-25087-8_19

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  • DOI: https://doi.org/10.1007/978-3-319-25087-8_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25086-1

  • Online ISBN: 978-3-319-25087-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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