Real-time face detection and recognition

Abstract

Face detection and recognition has received substantial attention from both research communities and the market over the past decades. It is one of the prominent research area due to its immense practical application in the area of biometric authentication, security system, criminal identification and human-computer interaction. Face recognition still remains challenging today due to wide range of faces, illumination conditions, limitation of the technology and algorithms. The aim of the project is to study various algorithms and develop a real-time face detection and recognition system. In this report a face detection algorithm call Viola–Jones is used for the implementation. It is currently the best algorithm used in real-time application due to its high accuracy and detection speed. For face recognition involves two step, extract facial features from the query image and compare the similarly against the database images. Features extraction algorithm can be classified into holistic and local approach. Principle Component Analysis (PCA) is a holistic approach where it takes the whole face images as input while Speed Up Robust Features (SURF) is a local approach that takes independent face regions as input. To find most similar image in database it can be done using either distance metrics or machine learning technique such as Artificial Neural Network (ANN). This report will present the structure of the system and how it is implemented. The results of the recognition between holistic and local approach will be presented in the last chapter.