Efficient face detection method with eye region judgment

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.