Gender Classification Using Single Frontal Image Per Person: Combination of Appearance and Geometric Based Features

Today, many social interactions and services depend on gender. In this paper, we introduce a single image gender classification algorithm using combination of appearance-based and geometric-based features. These include Discrete Cosine Transform (DCT), and Local Binary Pattern (LBP), and geometrical distance feature (GDF). The novel feature, GDF proposed in this paper, is inspired from physiological differences between male and female faces. Combination of appearance-based features (DCT and LBP) with geometric-based feature (GDF) leads to higher gender classification accuracy. Our system estimates gender of the input image based on the majority rule. If the results of DCT and LBP features are not identical, gender classification will be based on GDF feature. The proposed method was evaluated on two databases: AR and ethnic. Experimental results show that the novel geometric feature improves the gender classification accuracy by 13%.

[1]  K.W. Bowyer,et al.  Learning to predict gender from iris images , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[2]  Yuchun Fang,et al.  Improving LBP features for gender classification , 2008, 2008 International Conference on Wavelet Analysis and Pattern Recognition.

[3]  Chiraz BenAbdelkader,et al.  A Local Region-based Approach to Gender Classi.cation From Face Images , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[4]  Abdesselam Bouzerdoum,et al.  A Shunting Inhibitory Convolutional Neural Network for Gender Classification , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[5]  A. Martínez,et al.  The AR face databasae , 1998 .

[6]  Karim Faez,et al.  An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System , 2003, EURASIP J. Adv. Signal Process..

[7]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[8]  Matthew Toews,et al.  Detection, Localization, and Sex Classification of Faces from Arbitrary Viewpoints and under Occlusion , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Shaogang Gong,et al.  Learning gender from human gaits and faces , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[10]  Mircea Nicolescu,et al.  Gender classification from hand shape , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[11]  Roope Raisamo,et al.  Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.