Gender Recognition Based on Weighted Combination of Gabor Wavelets

Most existing gender recognition methods were tested on specific face databases. However, they are faced with big challenges in practical applications due to the complexity and diversity of human faces. This paper presents a novel gender recognition method based on weighted combination of Gabor wavelet transform. Firstly, the face image is preprocessed to remove the influence of light and posture. We then utilize Gabor wavelet transform to generate feature vectors of the face image and construct a weight matrix to combine the Gabor features into a robust feature based on gradient direction. It reduces the redundancy greatly and, at the same time, highlights the most valuable components of combined features. Secondly, we use principal component analysis to further reduce the dimension of the obtained feature. Finally, we put a large number of extracted features from training samples into support vector machine to train a binary gender classifier. The experimental results show that the proposed method achieves higher accuracy on both the public face database and the face database collected from practical applications when compared to state-of-the-art methods.