Recognizing Gender from Human Facial Regions using Genetic Algorithm

Recently, recognition of gender from facial images has gained a lot of importance. There exist a handful of research work that focus on feature extraction to obtain gender specific information from facial images. However, analyzing different facial regions and their fusion help in deciding the gender of a person from facial images. In this paper, we propose a new approach to identify gender from frontal facial images that is robust to background, illumination, intensity, and facial expression. In our framework, first the frontal face image is divided into a number of distinct regions based on facial landmark points that are obtained by the Chehra model proposed by Asthana et al. The model provides 49 facial landmark points covering different regions of the face, e.g. forehead, left eye, right eye, lips. Next, a face image is segmented into facial regions using landmark points and features are extracted from each region. The Compass LBP feature, a variant of LBP feature, has been used in our framework to obtain discriminative gender specific information. Following this, a Support Vector Machine based classifier has been used to compute the probability scores from each facial region. Finally, the classification scores obtained from individual regions are combined with a genetic algorithm based learning to improve Avirup Bhattacharyya E-mail: [email protected] Department of Electronics and Communication Engineering, Institute of Engineering and Management, Kolkata, India. Rajkumar Saini E-mail: [email protected] Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India. Partha Pratim Roy E-mail: [email protected] Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India. Debi Prosad Dogra E-mail: [email protected] School of Electrical Sciences, Indian Institute of Technology, Bhubaneswar, India. Samarjit Kar E-mail: [email protected] Department of Mathematics, National Institute of Technology, Durgapur, India. ar X iv :1 71 2. 01 66 1v 1 [ cs .C V ] 5 D ec 2 01 7 2 Avirup Bhattacharyya et al. the overall classification accuracy. The experiments have been performed on popular face image datasets such as Adience, cFERET (color FERET), LFW and two sketch datasets, namely CUFS and CUFSF. Through experiments, we have observed that, the proposed method outperforms existing approaches.