Efficient face detection method with eye region judgment

Chun-Fu Lin,Sheng-Fuu Lin

doi:10.1186/1687-5281-2013-34

Abstract

Real-time face detection technology can be applied in many industrial or commercial products. Many face detection applications use the traditional Adaboost face detection system which is proposed by Viola and Jones in 2004. Viola and Jones used the Adaboost training algorithm and the Haar-like features in their proposed traditional Adaboost face detection system, which has a high detection rate but long training time. Many studies have attempted to reduce the training time and retain the high detection rate of the traditional Adaboost face detection system. However, the detection rate of the Adaboost-based face detection system cannot compete with the traditional Adaboost face detection system when the training time is reduced significantly. This study proposes the judging existence of eye region (JEER) method to enhance the detection rate of the previous Adaboost-based face detection systems. The eyes are more salient and representative features than the other facial parts such as the mouth or ears, especially when mask is worn on the human face. Therefore, the proposed face detection system with the JEER method achieves higher detection rate than the Adaboost-based face detection systems. Although the JEER computation results in a slightly longer training time, the training time of the proposed face detection system is still much shorter than the traditional Adaboost face detection system owing to the efficient JEER computation. The experimental results obtained using the gray FERET and CMU databases show that the proposed face detection system is effective in detection and efficient in training.

Highlights

Face detection technology is important owing to its applicability in many fields such as in statistics and security services [1,2]
The present study proposes the judging existence of eye region (JEER) algorithm, which is suitable for low-resolution facial image in natural images, to enhance the detection rate of the Adaboost-based face detection systems
JEER is applied to the Adaboost-based face detection system, which is a hybrid of probability-based face mask pre-filtering (PFMPF) and pixel-based hierarchical feature Adaboosting (PBHFA), proposed by Guo et al The proposed face detection system achieves a higher detection rate and a lower false positive rate than the Adaboost-based face detection system which is hybrid from PFMPF and PBHFA, and the training time of the proposed system is still much shorter than the traditional Adaboost face detection system

Summary

Introduction

Face detection technology is important owing to its applicability in many fields such as in statistics and security services [1,2]. Many useful face detection systems use various basic theories [3,4,5,6] Among these basic theories, Viola and Jones chose the Adaboost algorithm to train every Haarlike feature for a weak classifier [7,8]. Some critical facial properties must be considered to enhance the detection rate of the Adaboost-based face detection systems, where the training time is much shorter than the traditional Adaboost face detection system. The present study proposes the judging existence of eye region (JEER) algorithm, which is suitable for low-resolution facial image in natural images, to enhance the detection rate of the Adaboost-based face detection systems. The traditional Adaboost face detection system employs the Adaboost algorithm to train each Haar-like feature fj for the weak classifier hj(x, fj, p, θ) in the detection window [8]. Algorithm 1 describes the Adaboost algorithm [8]

Background

Method

Conclusion