Improved gender classification using Discrete Wavelet Transform and hybrid Support Vector Machine

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Jakub Walczak
Adam Wojciechowski

Keywords : computer vision, gender recognition, DWT, SVM
Gender recognition, across different races and regardless of age, is becoming an increasingly important technology in the domains of marketing, human-computer interaction and security. Most state-of-the-art systems consider either highly constrained conditions or relatively large databases. In either case, often not enough attention is paid to cross-racial age-invariant applications. This paper proposes a~method of hybrid classification, which performs well even with a small training set. The design of the classifier enables the construction of reliable decision boundaries insensitive to an aging model as well as to race variation. For a training set consisting of one hundred images, the proposed method reached an accuracy level of 90%, whereas the best method known from the literature, tested under the restrictions imposed on the database, achieved only 78% accuracy.

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Walczak, J., & Wojciechowski, A. (2016). Improved gender classification using Discrete Wavelet Transform and hybrid Support Vector Machine. Machine Graphics and Vision, 25(1/4), 27–34.

P. J. Phillips, J. Huang H. Wechsler and, and P. Rauss. The FERET database and evaluation procedure for face recognition. Image and Vision Computing, 13:259–306, 1998. (Crossref)

P. Phillips, H. Mon, S. A. Rizvi, and P. J. Rauss. The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence, 22:1090–1104, 2000. (Crossref)

M. Nazir, M. Ishtiaq, A. Batool, M. A. Jaffar, and A. M. Mirza. Feature selection for efficient gender classification. In Recent Advances in Neural Networks, Fuzzy Systems & Evolutionary Computing. Proc. 11th WSEAS Int. Conf. on Neural Networks and 11th WSEAS Int. Conf. on Evolutionary Computing and 11th WSEAS Int. Conf. on Fuzzy Systems, NN’10/EC’10/FS’10, pages 70–75, IASI, Romania, 2010.

V. Singh, V. Shokeen, and B. Singh. Comparison of feature extraction algorithms for gender classification. International Journal of Engineering Research and Technology, 2(5):1313–1318, May 2013.

H. A. Alrashed and M. A. Berbar. Facial gender recognition using eyes images. International Journal of Advanced Research in Computer and Communication Engineering, 2(6):2441–2445, 2013.

L. Spacek. Collection of facial images: Faces94. The Ohio State University. Online; accessed 09 Sep 2016.

A. M. Martinez. AR Face Database. The Ohio State University. Online; accessed 09 Sep 2016.

A. M. Martinez and R. Benavente. The AR face database. CVC Technical Report #24, June 1998.

A. Wojciechowski. Potential field based camera collisions detection in a static 3D environment. Machine Graphics & Vision, 15(3/4):665, 2006.

R. Sharma and M. S. Patterh. Indian face age database: A database for face recognition with age variation. International Journal of Computer Applications (0975 -8887), 126(5):21–27, 2015. (Crossref)

V. N. Pawar and S. N. Talbar. Hybrid machine learning approach for object recognition: Fusion of features and decisions. Machine Graphics and Vision, 19(4):411–428, 2010.

R. Staniucha and A. Wojciechowski. Mouth features extraction for emotion classification. In Proc. 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 1685–1692. IEEE, Sept 2016. (Crossref)

A. Katharotiya, S. Patel, and M. Goyani. Comparative analysis between DCT & DWT techniques of image compression. Journal of Information Engineering and Applications, 1(2):9–17, 2011.

L. Aguado, I. Serrano-Pedraza, S. Rodriguez, and F. J. Roman. Effects of spatial frequency content on classification of face gender and expression. The Spanish Journal of Psychology, 13(2):525–537, 2010. (Crossref)

S.A. Khan, M. Katameneni, and P. M. Latha. A comparative analysis of gender classification techniques. Middle-East Journal of Scientific Research, 20(1):1–13, 2014.

A. Amine, S. Ghouzali, M. Rziza, and D. Aboutajdine. An improved method for face recognition based on SVM in frequency domain. Machine Graphics and Vision, 18(2):187–199, 2009.



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