Image-based Face Detection and Recognition: "State of the Art"

TL;DR

This paper reviews and evaluates various face detection and recognition methods for images, aiming to improve accuracy and response rate in surveillance systems by testing on diverse face databases.

Contribution

It provides a comprehensive evaluation and a complete solution for image-based face detection and recognition with improved accuracy and speed.

Findings

Enhanced face recognition accuracy across diverse conditions

Faster response times in face detection systems

Effective handling of pose, emotion, and lighting variations

Abstract

Face recognition from image or video is a popular topic in biometrics research. Many public places usually have surveillance cameras for video capture and these cameras have their significant value for security purpose. It is widely acknowledged that the face recognition have played an important role in surveillance system as it doesn't need the object's cooperation. The actual advantages of face based identification over other biometrics are uniqueness and acceptance. As human face is a dynamic object having high degree of variability in its appearance, that makes face detection a difficult problem in computer vision. In this field, accuracy and speed of identification is a main issue. The goal of this paper is to evaluate various face detection and recognition methods, provide complete solution for image based face detection and recognition with higher accuracy, better response rate as an initial step for video surveillance. Solution is proposed based on performed tests on various face rich databases in terms of subjects, pose, emotions, race and light.