Face Recognition and Gender Classification Using LBP Ramakalaivani E.1,*, Dharanidevi T.S.2, Priya A. Divya2, Kohila C.2 1Assistant Professor, Department of Computer Science and Engineering, Karpagam College of Engineering 2Student, Department of Computer Science and Engineering, Karpagam College of Engineering *Corresponding author: E. Ramakalaivani, E-mail: ramakalaivani.e@gmail.com
Online published on 30 April, 2019. Abstract The identification of human beings is based on their physical body parts such as the face, eyes, nose, iris, ear, fingerprint, voice plays an important role in electronic applications and has become a popular area of research in image processing and in many face recognition related fields. Facial Expression Recognition (FER) is a decisive technology and a challenging task for human-computer interaction. Out of all the above-mentioned body parts, the face is one of the most popular traits because of its unique and differing features. In fact, individuals can process a face in a variety of ways to classify it by its identity, along with any numbers of other characteristics, such as gender, ethnicity, and age. The classification has become prominent as a leading technique for problem solution and optimization. The classification has been used extensively in many problems that are controlled by a particular ruler or government. It is an area of great significance and has great potential for future research for future gender classification. It offers more than two but not many industrial applications in near future such as monitoring, surveillance, commercial profiling, and human-computer interaction. Many different methods have been proposed for gender classification like gait, iris and hand shape. However, the majority of techniques for gender classification are based on facial expression recognition. The proposed system uses skin tone extraction, facial feature exaction and feature extraction are done using LBP. Classification is done using Artificial Neural Network from that male and female is recognized. Top Keywords Facial Expression recognition, self-organizing map, Artificial neural network, artificial intelligence. Top |