Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Intelligent biometric techniques in fingerprint and face recognition
Intelligent biometric techniques in fingerprint and face recognition
Biometric Identification through Hand Geometry Measurements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics: Personal Identification in Networked Society
Biometrics: Personal Identification in Networked Society
CBIR over multiple projections of 3D objects
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
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A human hand recognition system is introduced. First, a simple preprocessing technique which extracts the palm, the four fingers, and the thumb is introduced. Second, the eigenpalm, the eigenfingers, and the eigenthumb features are obtained using a fast incremental principal non-Gaussian directions analysis algorithm, called IPCA-ICA. This algorithm is based on merging sequentially the runs of two algorithms: the principal component analysis (PCA) and the independent component analysis (ICA) algorithms. It computes the principal components of a sequence of image vectors incrementally without estimating the covariance matrix (so covariance-free) and at the same time transforming these principal components to the independent directions that maximize the non-Gaussianity of the source. Third, a classification step in which each feature representation obtained in the previous phase is fed into a simple nearest neighbor classifier. The system was tested on a database of 20 people (100 hand images) and it is compared to other algorithms.