Face Detection Using Eigenface and Neural Network

Face recognition is a popular research topic in the biometric identification area. Face detection and localization is the most important pre-processing module of a face recognition system. The purpose of face detection is to search and localize the positions of faces in an image in the varied background. In this paper, we propose a face detection system which combines the eigenface algorithm with the neural network. The candidate face region is examined to see if it is a face image block by analyzing the geometrical distribution of the edges in the region. In our experiments, the proposed face detection system has a high face detection rate and less false detections.

I. Introduction

Biometric technology utilizes the biological characteristics of human bodies or behaviors as identification or verification features. The frequently used biometric features include face, fingerprint, voiceprint, and iris recognition. And the signature, handwriting, and walking type belong to the behavioral features. The fingerprint recognition is the most popular adopted in our daily lives. However, the sweat and the dust may reduce the accuracy sometimes. In face recognition system, it is not necessary to have physical contact with the machine and the image can be captured naturally by using a video camera. This makes face recognition a very convenient biometric identification approach. The face detection is an important preprocessing step for face recognition.