LAUSR

Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method.