— This paper concerns with a high performance algorithm for human face detection in still image. The algorithm has been developed for increasing the face detection rate under different environmental conditions. In this paper, the skin color detector is modified by combining with the low pass filter, Sobel edge detector and the modified Viola Jones eye detector. The Haar cascade classifier is modified by using the Clustering algorithm. The modified algorithms are then combined together with the Unscented Kalman filter (UKF) in our process. The use of the UKF in case of the face detection algorithm simply reflects the novelty of our paper. The UKF is used for removing the film grain noise from the still image. To clarify the effectiveness of our proposed algorithm, we compare our proposed algorithm with other face detection algorithms through the benchmark tests using different facial databases. The ROC curve clarifies the effectiveness of our proposed algorithm with best face detection results of 98.3%.
— Face detection, modified Haar cascade classifier, modified skin color detector, the Unscented Kalman filter.
Bikash Lamsal and Naofumi Matsumoto are with the Department of Information and Production Engineering, Ashikaga Institute of Technology, Ohmae-cho268-1, Ashikaga-shi, Tochigi prefecture, Japan (e-mail: email@example.com, firstname.lastname@example.org).
Cite: Bikash Lamsal and Naofumi Matsumoto, " Effects of the Unscented Kalman Filter Process for High Performance Face Detector," International Journal of Information and Electronics Engineering vol. 5, no. 6, pp. 454-459, 2015.