| Title |
Design of Face Recognition and Tracking System by Using RBFNNs Pattern Classifier with Object Tracking Algorithm |
| Authors |
오승훈(Oh, Seung-Hun) ; 오성권(Oh, Sung-Kwun) ; 김진율(Kim, Jin-Yul) |
| DOI |
https://doi.org/10.5370/KIEE.2015.64.5.766 |
| Keywords |
Particle Filter ; RBFNNs pattern classifier ; AdaBoost ; Particle Swarm Optimization(PSO) |
| Abstract |
In this paper, we design a hybrid system for recognition and tracking realized with the aid of polynomial based RBFNNs pattern classifier and particle filter. The RBFNN classifier is built by learning the training data for diverse pose images. The optimized parameters of RBFNN classifier are obtained by Particle Swarm Optimization(PSO). Testing data for pose image is used as a face image obtained under real situation, where the face image is detected by AdaBoost algorithm. In order to improve the recognition performance for a detected image, pose estimation as preprocessing step is carried out before the face recognition step. PCA is used for pose estimation, the pose of detected image is assigned for the built pose by considering the featured difference between the previously built pose image and the newly detected image. The recognition of detected image is performed through polynomial based RBFNN pattern classifier, and if the detected image is equal to target for tracking, the target will be traced by particle filter in real time. Moreover, when tracking is failed by PF, Adaboost algorithm detects facial area again, and the procedures of both the pose estimation and the image recognition are repeated as mentioned above. Finally, experimental results are compared and analyzed by using Honda/UCSD data known as benchmark DB. |