Face Recognition Using Map Discriminant on YCbCr Color Space
This is an incremental improvement for face recognition systems, specifically enhancing accuracy for applications like security or identification.
The paper tackled face recognition by applying maximum a posteriori discriminant on YCbCr color space to incorporate skin information, resulting in a 4% improvement in recognition rate and lower equal error rate compared to grayscale methods, though with three times more computation time.
This paper presents face recognition using maximum a posteriori (MAP) discriminant on YCbCr color space. The YCbCr color space is considered in order to cover the skin information of face image on the recognition process. The proposed method is employed to improve the recognition rate and equal error rate (EER) of the gray scale based face recognition. In this case, the face features vector consisting of small part of dominant frequency elements which is extracted by non-blocking DCT is implemented as dimensional reduction of the raw face images. The matching process between the query face features and the trained face features is performed using maximum a posteriori (MAP) discriminant. From the experimental results on data from four face databases containing 2268 images with 196 classes show that the face recognition YCbCr color space provide better recognition rate and lesser EER than those of gray scale based face recognition which improve the first rank of grayscale based method result by about 4%. However, it requires three times more computation time than that of grayscale based method.