Online Training for Face Recognition System Using Improved PCA
Keywords:face recognition, illumination, improved PCA, service robot, ITS face database
The variation in illumination is one of the main challenging problem for face recognition. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals. Recognizing face reliably across changes in pose and illumination using PCA has proved to be a much harder problem because eigenfaces method comparing the intensity of the pixel. To solve this problem, this research proposes an online face recognition system using improved PCA for a service robot in indoor environment based on stereo vision. Tested images are improved by generating random values for varying the intensity of face images. A program for online training is also developed where the tested images are captured real-time from camera. Varying illumination in tested images will increase the accuracy using ITS face database which its accuracy is 95.5 %, higher than ATT face database’s as 95.4% and Indian face database’s as 72%. The results from this experiment are still evaluated to be improved in the future.
Adini, Y.; Moses, Y. & Ulman, S. (1997). Face Recognition: The Problem Of Compensating for Changes in Illumination Direction. IEEE Transactions Pattern Analysis and Machine Intelligence,19 (7), 721-732.
Belhumeur, P., Kriegman, D. (1998). What is the set of images of an object under all possible illumination conditions. International Journal of Computer Vision, 28 (3), 245-260.
Etemad, K., Chellappa R (1997). Discriminant Analysis for Recognition of Human Face Images. Journal of the Optical Society of America, 14 (8), 1724-1733.
Turk M. and Pentland A. (1991). Face Recognition Using Eigenfaces. Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, 586-591.
Yang, M. (2000). Detecting Faces Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Inteligence, 24 (1), 34-58.
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