Binary neural network based 3D facial feature localization
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Face verification with a kernel fusion method
Pattern Recognition Letters
Combination of kernels applied to face verification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
3D facial feature localization for registration
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
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In this paper face verification techniques have been performed over 3D data acquired by a Laser Scanner. Advantages of 3D face models have been used to perform a normalization task over each face. First, an original method to detect local features points, based on Spin Images, has been developed. Once local features, as the nose tip or eyes corners, have been detected, a normalization process is carried out. After face normalization, different depth maps are calculated using several transform functions to equalize the images. The adequacy of each equalization to face verification has been measured to determine which one emphasizes most the feature discrimination. Face verification has been performed through a Principal Component Analysis and a Support Vector Machine. Final results show the importance of a careful 3D normalization and an optimal election of the depth map towards a improvement in the verification method.