Multimodal 2D, 2.5D & 3D Face Verification Export

@proceedings{citeulike:5960930, abstract = {A multimodal face verification process is presented for standard 2D color images, 2.5D range images and 3D meshes. A normalization in orientation and position is essential for 2.5D and 3D images to obtain a corrected frontal image. This is achieved using the spin images of the nose tip and both eyes, which feed an SVM classifier. First, a traditional principal component analysis followed by an SVM classifier are applied to both 2D and 2.5D images. Second, an iterative closest point algorithm is used to match 3D meshes. In all cases, the equal error rate is computed for different kinds of images in the training and test phases. In general, 2.5D range images show the best results (0.1\% EER for frontal images). A special improvement in success rate for turned faces has been obtained for normalized 2.5D and 3D images compared to standard 2D images}, author = {Conde, C. and Serrano, A. and Cabello, E.}, citeulike-article-id = {5960930}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ICIP.2006.312863}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4106966}, day = {20}, doi = {10.1109/ICIP.2006.312863}, journal = {Image Processing, 2006 IEEE International Conference on}, keywords = {face, image-processing}, month = {February}, pages = {2061--2064}, posted-at = {2009-10-18 03:23:14}, priority = {2}, title = {Multimodal 2D, 2.5D \& 3D Face Verification}, url = {http://dx.doi.org/10.1109/ICIP.2006.312863}, year = {2007} }

TY - CONF ID - citeulike:5960930 L3 - citeulike-article-id:5960930 N2 - A multimodal face verification process is presented for standard 2D color images, 2.5D range images and 3D meshes. A normalization in orientation and position is essential for 2.5D and 3D images to obtain a corrected frontal image. This is achieved using the spin images of the nose tip and both eyes, which feed an SVM classifier. First, a traditional principal component analysis followed by an SVM classifier are applied to both 2D and 2.5D images. Second, an iterative closest point algorithm is used to match 3D meshes. In all cases, the equal error rate is computed for different kinds of images in the training and test phases. In general, 2.5D range images show the best results (0.1% EER for frontal images). A special improvement in success rate for turned faces has been obtained for normalized 2.5D and 3D images compared to standard 2D images JF - Image Processing, 2006 IEEE International Conference on EP - 2064 TI - Multimodal 2D, 2.5D & 3D Face Verification SP - 2061 KW - face KW - image-processing AU - Conde, C AU - Serrano, A AU - Cabello, E PY - 2007/02/20/ UR - http://dx.doi.org/10.1109/ICIP.2006.312863 ER -