A.S. Sochenkova, I.V. Sochenkov, A.V. Vokhmintsev Inverted Indexing of Images for Face Search and Recognition
There are many different possible applications for face recognition, but this problem has not been solved properly. Therefore face recognition is still very important task. As an example of its application, face recognition could be useful for security systems to provide safety. For these systems it is necessary to identify the person among many others. In this case this work presents new approach in data indexing, which provides fast retrieval in big image collections. Data indexing in this research has five stages. First, we detect and extract the area containing face, second we align face, and then we detect areas containing eyes and eyebrows, nose, mouth. On the next stage we find key points of each area using histograms of oriented gradients (HOG) descriptors and finally index these descriptors with help of quantization procedure. The experimental analysis of this method is also performed. This paper shows that performing method has results at the level of state-ofthe-art face recognition methods, but it also gives results fast that is important for the systems that provide safety.
face recognition, person identification, inverted index
1. Chen, B.C., Chen, C.S., and Hsu, W.H, "Cross-age reference coding for age-invariant face recognition and retrieval," In European Conference on Computer Vision. Springer International Publishing, 768-783 (2014, September).
2. Ren, S., Cao, X., Wei, Y., and Sun, J., "Face alignment at 3000 fps via regressing local binary features," Proc. IEEE Conference on Computer Vision and Pattern Recognition, 1685-1692 (2014).
3. Echeagaray-Patron, B. A., and Kober, V., "3D face recognition based on matching of facial surfaces," Proc. SPIE Optical Engineering+ Applications. International Society for Optics and Photonics, 95980V-95980V (2015, September).
4. Lee, Y., Song, H., Yang, U., Shin, H., and Sohn, K., "Local feature based 3D face recognition," Proc. International Conference on Audio-and Video-based Biometric Person Authentication. Springer Berlin Heidelberg, 909-918 (2005, July).
5. Vokhmintcev, A.V., Sochenkov, I.V., Kuznetsov, V.V., and Tikhonkikh, D.V., "Face recognition based on a matching algorithm with recursive calculation of oriented gradient histograms," In Doklady Mathematics, Pleiades Publishing, vol. 93, no. 1, 37-41 (2016, January).
6. Sun, Y., Liang, D., Wang, X., and Tang, X., "Deepid3: Face recognition with very deep neural networks," arXiv preprint arXiv:1502.00873. (2015)
7. Godil, A., Ressler, S., and Grother, P., "Face recognition using 3D facial shape and color map information: comparison and combination," In Defense and Security. International Society for Optics and Photonics, 351-361 (2004, August)
8. Viola, P. and Jones, M.J., "Robust real-time face detection," International Journal of Computer Vision, vol. 57, no. 2, 137– 154 (2004)
9. Viola, P. and Jones, M.J., "Rapid Object Detection using a Boosted Cascade of Simple Features," Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2001), (2001).
10. Dalal, N., and Triggs, B., "Histograms of oriented gradients for human detection," Proc. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). IEEE, vol. 1, 886-893 (June 2005)
11. Squire, D., Muller, W., Muller, H., and Raki, J., "Content-based query of image databases, inspirations from text retrieval: inverted files, frequency-based weights and relevance feedback," (1999).
12. Sochenkov, I., and Vokhmintsev, A., "Visual Duplicates Image Search for a Non-cooperative Person Recognition at a Distance," Procedia Engineering, 129, 440-445 (2015).
13. Kemelmacher-Shlizerman, I., Seitz, S., Miller, D., and Brossard, E., "The megaface benchmark: 1 million faces for recognition at scale," arXiv preprint arXiv:1512.00596. (2015).
14. Huang, G. B., Ramesh, M., Berg, T., and Learned-Miller, E., "Labeled faces in the wild: A database for studying face recognition in unconstrained environments," Technical Report 07-49, University of Massachusetts, Amherst, vol. 1, no. 2, 3 (2007).
15. Slesarev A. V. i dr. Yandeks na ROMIP 2010: Poisk pohozhih izobrazhenij i dublikatov //Rossijskij seminar po Ocenke Metodov Informacionnogo Poiska. Trudy ROMIP. – 2010. – S. 148-153.