Depth range component based 3D face recognition using fuzzy methods

The face shape using depth information in the face represents personal features in detail. In particular, the surface curvatures extracted from the face contain the most important personal facial information. In this paper, we develop a method for recognizing range face images by combining the multiple-face-regions (region component based), using fuzzy integral. For the proposed approach, the first step uses face curvatures that helps extract facial features for range face images, after normalization using the SVD. As a result of this process, we obtain curvature feature for each region range face. The second step of approach concerns the application of PCA and Fisherface method to each component range face. The reason for adapted PCA and Fisherface method is these can maintain the surface attribute for face curvature, even though these can generate the reduced image dimension. In the last step, the aggregation of the individual classifiers using the fuzzy integral and the fuzzy neural network (CAFNN) are explained for each region component based. The experimental results obtained that the approach presented in this paper have outstanding classification in comparison to the results obtained by other methods.