Color face recognition using normalized-discriminant hybrid color space and quaternion moment vector features

The Zernike and pseudo-Zernike moments (ZMs/PZMs) have been found useful for a variety of applications requiring feature extraction due to their favorable properties, such as low information redundancy, rotational invariance, higher noise resilience and extendibility to the color space. In this paper, we propose an approach for color face recognition based on Zernike/pseudo-Zernike quaternion moment vector (QMV) features and a novel normalized-discriminant hybrid color space. The proposed XnSBr color space is composed by taking Xn from the normalized-XYZ, S from HSV and Br from the discriminant RGB-r color spaces to capture the features efficiently. In addition, we propose the use of quaternion vector distance (QVD) similarity measure for the QMV features in order to enhance the recognition accuracy. The exhaustive comparative performance analyses with the state-of-the-art approaches in the different color spaces demonstrate the superiority of the proposed approach in terms of accuracy and speed.