Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Fractal image compression: theory and application
Fractal image compression: theory and application
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Use of depth and colour eigenfaces for face recognition
Pattern Recognition Letters
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face Modeling and Recognition in 3-D
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition
Computer Vision and Image Understanding
Face recognition by fractal transformations
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Journal of Cognitive Neuroscience
Expression-invariant 3D face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Evaluation of automatic 4D face recognition using surface and texture registration
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Rank-Based decision fusion for 3d shape-based face recognition
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Occluded face recognition by means of the IFS
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Face recognition using the weighted fractal neighbor distance
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-fisher: a new LDA-based face recognition algorithm with selection of principal components
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental Linear Discriminant Analysis for Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
FIRE: fractal indexing with robust extensions for image databases
IEEE Transactions on Image Processing
A range/domain approximation error-based approach for fractal image compression
IEEE Transactions on Image Processing
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
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This work investigates face recognition based on normal maps, and the performance improvement that can be obtained when exploiting it within a multimodal system, where a further independent module processes visible images. We first propose a technique to align two 3D models of a face by means of normal maps, which is very fast while providing an accuracy comparable to well-known and more general techniques such as Iterative Closest Point (ICP). Moreover, we propose a matching criterion based on a technique which exploits difference maps. It does not reduce the dimension of the feature space, but performs a weighted matching between two normal maps. In the second place, we explore the range of performances offered by different linear and nonlinear classifiers, when applied to the normal maps generated from the above aligned models. Such experiments highlight the added value of chromatic information contained in normal maps. We analyse a solid list of classifiers which were selected due to their historical reference value (e.g. Principal Component Analysis) or to their good performances in the bidimensional setting (Linear Discriminant Analysis, Partitioned Iterated Function Systems). Last but not least, we perform experiments to measure how different ways of combining normal maps and visible images can enhance the results obtained by the single recognition systems, given that specific characteristics of the images are taken into account. For these last experiments we only consider the classifier giving the best average results in the preceding ones, namely the PIFS-based one.