AUTOMATIC HUMAN FACE RECOGNITION USING MULTIVARIATE GAUSSIAN MODEL AND FISHER LINEAR DISCRIMINATIVE ANALYSIS

Abstract

Face recognition plays an important role in surveillance, biometrics and is a popular application of computer vision. In this paper, color based skin segmentation is proposed to detect faces and is matched with faces from the dataset. The proposed color based segmentation method is tested in different color spaces to identify suitable color space for identification of faces. Based on the sample skin distribution a Multivariate Gaussian Model is fitted to identify skin regions from which face regions are detected using connected components. The detected face is match with a template and verified. The proposed method Multivariate Gaussian Model – Fisher Linear Discriminative Analysis (MGM – FLDA) is compared with machine learning - Viola & Jones algorithm and it gives better results in terms of time.

Keywords

• Face Recognition
• Multivariate Gaussian Model
• Image Segmentation
• Connected Component
• Color Spaces