Abstract
Gender recognition is a relevant problem due to the number and importance of its possible application areas. The challenge is to achieve high recognition rates in the shortest possible time. Most studies are based on Local Binary Patterns (LBP) and its variants to estimate gender. In this paper, we propose the use of Binary Robust Independent Elementary Features (BRIEF), Oriented FAST and Rotated BRIEF (ORB) and Binary Robust Invariant Scalable Keypoints (BRISK) in gender recognition due to their good performance and speed. The aim is to show that ORB and BRISK are faster than LBP but allow to achieve similar recognition rates, which makes them suitable for real-time systems. For the best of our knowledge, it has not been studied in literature.
Keywords
Download to read the full chapter text
Chapter PDF
References
Ahonen, T., Hadid, A., Piettikäinen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on pattern Analysis and Machine Intelligence 28(12) (2006)
Ahonen, T., Matas, J., Piettikäinen, M., He, C.: Rotation invariant image description with local binary pattern histogram fourier features. Scandinavian Conference on Image Analysis, SCIA (2009)
Andreu, Y., García-Sevilla, P., Mollineda, R.A.: Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes. Image and Vision Computing 32(1), 27 (2014)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
BekiosCalfa, J., Buenaposada, J.M., Baumela, L.: Revisiting linear discriminant techniques in gender recognition. IEEE Transactions on pattern Analysis and Machine Intelligence 33(4), 858–864 (2011)
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)
Leutenegger, S., Chli, M., Siegwart, R.Y.: Brisk: Binary robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555. IEEE (2011)
Li, M., Bao, S., Dong, W., Wang, Y., Su, Z.: Head-shoulder based gender recognition. In: IEEE International Conference on Image Processing (ICIP), pp. 2753–2756 (2013)
Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision 1999, vol. 2, pp. 1150–1157 (1999)
Lu, L., Shi, P.: A novel fusion-based method for expression-invariant gender classification. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009, pp. 1065–1068 (2009)
Muja, M., Lowe, D.: Fast matching of binary features. In: 2012 Ninth Conference on Computer and Robot Vision (CRV), pp. 404–410 (2012)
Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The feret database and evaluation procedure for face recognition algorithms. Image and Vision Computing 16(5), 295–306 (1998)
Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. In: Computational Imaging and Vision, vol. 40, Springer (2011)
Ramón-Balmaseda, E., Lorenzo-Navarro, J., Castrillón-Santana, M.: Gender classification in large databases. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 74–81. Springer, Heidelberg (2012)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an eficient alternative to sift or surf. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)
Thomaz, C.E., Giraldi, G.A.: A new ranking method for principal components analysis and its application to face image analysis. Image and Vision Computing 28(6), 902–913 (2010)
Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)
Wang, J.G., Li, J., Yau, W.Y., Sung, E.: Boosting dense sift descriptors and shape contexts of face images for gender recognition. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 96–102 (2010)
Wang, J.G., Wang, H.L., Ye, M., Yau, W.Y.: Real-time gender recognition with unaligned face images. In: 2010 The 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 376–380 (June 2010)
Ylioinas, J., Hadid, A., Pietikäinen, M.: Combining contrast information and local binary patterns for gender classification. In: Image Analysis, pp. 676–686 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Iglesias, F.S., Buemi, M.E., Acevedo, D., Jacobo-Berlles, J. (2014). Evaluation of Keypoint Descriptors for Gender Recognition. In: Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_69
Download citation
DOI: https://doi.org/10.1007/978-3-319-12568-8_69
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12567-1
Online ISBN: 978-3-319-12568-8
eBook Packages: Computer ScienceComputer Science (R0)