THE APPLICATION OF IMAGE ENHANCEMENT METHOD FOR FACE RECOGNITION SYSTEMS

Abstract

Three-step face recognition algorithm which includes non-linear enhancement (dynamic range compression) and faces localization on the basis of skin color segmentation with subsequent extraction of anthropometric face points is proposed. The process of face recognition on the basis of principal component analysis is also considered.

Full Text

Face recognition has always caused great interest in computer vision, especially in connection with increasing practical needs such as biometrics, search engines, video compression, video conferencing systems, computer vision in robotics, intelligent security and access control systems. Face recognition algorithms can be divided into two categories: methods based on extracting features of images and methods based on representation of a facial image. The first group of methods uses properties and geometric relationships such as areas, distances and angles between feature points of a facial image. The second group of methods considers global features of a facial image. Usually these methods try to represent facial data more efficiently, for example, as a set of main vectors. Typically, a face recognition algorithm includes three steps: image preprocessing, face localization, face recognition. In this paper we present an algorithm which includes nonlinear image enhancement (dynamic range compression), face localization on the basis of skin color segmentation and face recognition on the basis of principal components analysis [1]. In practice images captured by digital devices often differ from what an observer remembers. It happens due to the fact that a camera captures the physical values of light data, while an observer's nervous system processes these data. For example, an observer can easily see details both in deep shadows and in illuminated areas while a capture device will get the given scene with too dark areas or light-struck areas.