Fuzzy-Appearance Manifold and Fuzzy-Nearest Distance Calculation for Model-Less 3D Pose Estimation of Degraded Face Images

Abstract

This paper presents the development of fuzzy-appearance manifold and fuzzy-nearest distance calculation in the eigenspace domain for pose estimation of degraded face images. In order to obtain a robust pose estimation system which can deal with the fuzziness of face data caused by statistical errors, we proposed the fuzzy-vector representation in eigenspace domain of the face images. Using fuzzy-vector representations, all of the crisp vectors of face data in the eigenspace domain are firstly transformed into fuzzy-vectors as fuzzy-points. Next, the fuzzy-appearance manifold is constructed from all the available fuzzy-points and the fuzzy-nearest distance calculation is proposed as the classifier of the pose estimation system. The pose estimation of an unknown face image is performed by firstly being projected onto the eigenspace domain then transformed to become an unknown fuzzy-point, and its fuzzy-distance with all of the available fuzzy-points in the fuzzy-appearance manifold will be calculated. The fuzzy-point in the manifold which has the nearest distance to that unknown fuzzy-point will be determined as the pose position of the unknown face image. In the experiment, face images with various quality degradation effects were used. The results show that the system could maintain high recognition rates for estimating the pose position of the degraded face images.