Abstract
The study of similarities and dissimilarities between human faces has been very active research topic for decades. The human face is composed of several textural representations, carrying discriminating information from one another mainly due to variations in age, gender, and facial expressions. Based on study and analysis of different facial features, the development of an accurate and efficient gender recognition system is an ultimate requirement for different useful applications such as surveillance, design of entrance and exit protocols at shopping malls, browsing of gender specific advertisement material on internet, etc. The gender recognition is essentially a binary classification process in which an input image is reported either true or false gender of the person in question. In this paper, we propose a knowledge based gender recognition system using histogram of oriented gradients (HOG) and local binary descriptor (namely LBP, Brisk and FREAK) based features, which can be used in robotics etc. To the best of our knowledge, it is the first time that Brisk and FREAK based features are used for the gender recognition problem. To evaluate our proposed system, we use standard IMM gender database (of 240 images) to extracted features and train (and test) the K nearest neighbors (KNN) classifier. Our results show that the proposed gender recognition system outperforms the existing methods when tested on above mentioned database, by giving accuracy as high as 90 %for FREAK features, and 94.16 % for HOG feature, improved by 6.5 %.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hu, Y., Yan, J., Shi, P.: A fusion-based method for 3D facial gender classification. In: The 2nd International Conference on Computer and Automation Engineering (ICCAE), vol. 5, pp. 369–372. IEEE Press, Singapore (2010)
Mozaffari, S., Behravan, H., Akbari, R.: Gender classification using single frontal image per person: combination of appearance and geometric based features. In: 20th International Conference on Pattern Recognition (ICPR), pp. 1192–1195. IEEE Press, Istanbul (2010)
Ko, J.B., Lee, W., Choi, S.E., Kim, J.: A gender classification method using age information. In: International Conference on Electronics, Information and Communication (ICEIC), pp. 1–2. IEEE Press, Kota Kinabalu (2014)
Bekhouche, S.E., Ouafi, A., Benlamoudi, A., Taleb-Ahmed, A., Hadid, A.: Facial age estimation and gender classification using multi level local phase quantization. In: 3rd International Conference on Control, Engineering and Information Technology (CEIT), pp. 1–4. IEEE Press, Tlemcen (2015)
Tao, Y.: Automated estimation of human age, gender and expression. EE368 Digital Image Processing Final Project Report, p. 4 (2014)
Choi, S., Han, S.: New binary descriptors based on BRISK sampling pattern for image retrieval. In: International Conference on Information and Communication Technology Convergence (ICTC), pp. 575–576. IEEE, Busan (2014)
Schaeffer, C.: A Comparison of Keypoint Descriptors in the Context of Pedestrian Detection: FREAK vs. SURF vs. BRISK (2013)
Wu, Y., Cheng, Z., Jing, W., Nan, W.: Image registration method based on SURF and FREAK. In: International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1–4. IEEE Press, Ningbo (2015)
Mäkinen, E., Raisamo, R.: Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 541–547 (2008). IEEE Press
Mousavi, B.S., Hirad, A.: Automatic gender classification using neuro fuzzy system. Indian J. Sci. Technol. 4(10), 1198–1201 (2011)
Data sets for Statistical Models of Shape. http://www.imm.dtu.dk/~aam/datasets/datasets.html
Lee, P., Timmaraju, A.S.: Learning binary descriptors from ımages
Nordstrøm, M.M., Larsen, M., Sierakowski, J., Stegmann, M.B.: The IMM face database-an annotated dataset of 240 face images. Technical University of Denmark, DTU Informatics, Building 321 (2004)
Leibstein, J., Findt, A., Nel, A.: Efficient texture classification using local binary patterns on graphics processing unit. In: Proceedings of the Twenty-First Annual Symposium of the Pattern Recognition Association of South Africa, pp. 147–152 (2010)
Spang, V., Henry, A.: Object Tracking Using Local Binary Descriptors (2014)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE Press, San Diego (2005)
Watanabe, T., Ito, S., Yokoi, K.: Co-occurrence histograms of oriented gradients for pedestrian detection. In: Wada, T., Huang, F., Lin, S. (eds.): PSIVT 2009. LNCS, vol. 5414. Springer, Heidelberg (2009). doi:10.1007/978-3-540-92957-4_4
Saatci, Y., Town, C.: Cascaded classification of gender and facial expression using active appearance models. In: 7th International Conference on Automatic Face and Gesture Recognition, pp. 393–398. IEEE Press, Washington, DC (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Noor, Z., Akram, M.U., Akhtar, M., Saad, M. (2016). Facial Texture Analysis for Recognition of Human Gender. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-47650-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47649-0
Online ISBN: 978-3-319-47650-6
eBook Packages: Computer ScienceComputer Science (R0)