Proceedings of International Conference On Global Innovations In Computing Technology (ICGICT'14) Human Gait Based Gender Classification Using Energy Motion Approaches

Abstract: Gender classification such as classifying human face is only challenging for computer, but even hard for human in some cases. In our work a new novel approach is proposed to recognize gender from the face image. Continuous Wavelet Transforms, Discrete Wavelet Transforms, Radon Transforms are used for features selections for each face images of male and female. These selected features will be used to classify the face images of each Gender using Support Vector Machine. Our work use ORL database contain 100 images include both Male and Female Gender. The experimental result shows that the proposed approach (Continuous wavelet Transform and Support Vector Machine). Proposed work achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head, hair and back are more discriminative than other components. All the above prove that gait-based gender classification is feasible in controlled environments. We use both static and dynamic data sets (images & video) for the gender classification process through SVM classifier

Proceedings of International Conference On Global Innovations In Computing Technology (ICGICT'14) Human Gait Based Gender Classification Using Energy Motion Approaches

Abstract: Gender classification such as classifying human face is only challenging for computer, but even hard for human in some cases. In our work a new novel approach is proposed to recognize gender from the face image. Continuous Wavelet Transforms, Discrete Wavelet Transforms, Radon Transforms are used for features selections for each face images of male and female. These selected features will be used to classify the face images of each Gender using Support Vector Machine. Our work use ORL database contain 100 images include both Male and Female Gender. The experimental result shows that the proposed approach (Continuous wavelet Transform and Support Vector Machine). Proposed work achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head, hair and back are more discriminative than other components. All the above prove that gait-based gender classification is feasible in controlled environments. We use both static and dynamic data sets ( images & video) for the gender classification process through SVM classifier