The Facial Features Extraction for Face Recognition Based on Geometrical Approach

Over the last two decades, several different techniques have been proposed for face recognition, which is one of the challenging areas of research in the field of image processing, pattern recognition and vision applications. Automatic human face identification system, e.g. security checks and crime investigation, etc. involves face recognition. The basic process consists of extraction of potential, facial features such as eyes, nose, mouth, eyebrows, etc. In the present paper, a geometrical face model proposed by Shi-Hong Jeng et al. for frontal face images is improved by the inclusion of ears and chin also as potential facial features, since it enhances the discrimination ability of the proposed face model during face recognition .The developed approach is divided into four main steps. The first step is pre processing, the goal of this step is to get rid of high intensity noises and to transform the input image into binary one. The second step includes a labeling process, which label the facial feature candidates by block by block. Then find the center, area and the orientation of each feature candidate. Third step is a geometrical model, used to measure relative distances and to locate the actual position of the entire facial features. Finally, the matching process. The modified face model has been experimented with test images and an enhanced success rate of 94% is achieved

1 Introduction

Automatic recognition of human faces is one of the challenging areas of research in the field of image processing pattern recognition and computer vision. A document image, in general may consists of text and pictures. The face recognition techniques have to be used to detect and recognize the human faces embedded in the pictures. Further any automatic human face identification system such as security checks, crime investigations, etc. involves face recognition. The facial feature extraction is considered as a basic pre-processing step for any robust face recognition system.