Classification of face images using local iterated function systems
Machine Vision and Applications
A multiexpert collaborative biometric system for people identification
Journal of Visual Languages and Computing
Normal maps vs. visible images: Comparing classifiers and combining modalities
Journal of Visual Languages and Computing
A doubly weighted approach for appearance-based subspace learning methods
IEEE Transactions on Information Forensics and Security
Fractal recognition of compact artifacts on color images
Pattern Recognition and Image Analysis
Face recognition using scale-adaptive directional and textural features
Pattern Recognition
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We present a method for performing face recognition based on the fractal neighbor distance (FND). The FND has previously been used for face recognition. What distinguishes our method from others is that we incorporate the use of localized weights with the FND. In a local-to-global feature matching approach, a set of localized weights is used with an algorithm based on the FND that searches for local features. A global score is then derived from each localized score. This set of weights is designed to concentrate around the eyes and nose region of the face, because they contain more discriminating features.