Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Handbook of Face Recognition
A Biometric Identification System Based on Eigenpalm and Eigenfinger Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Palmprint Authentication (International Series on Biometrics)
Palmprint Authentication (International Series on Biometrics)
Journal of Cognitive Neuroscience
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In this paper we describe a number of experiments relating to PCA-based palmprint and face recognition. The experiments were designed to determine the influence of different training sets used for the construction of the eigenpalm and eigenface spaces on the recognition efficiency of biometric systems. The results of the recognition experiments, obtained using three palmprint databases (PolyU, FER1, FER2) and one face database (XM2VTSDB), suggest that it is possible to design a biometric recognition system that is robust enough to successfully recognize palmprints (or faces) even in cases when the eigenspaces are constructed from completely independent sets of palmprints or face images. Furthermore, the experiments show that for PCA-based face-recogni-tion systems with an eigenspace that is constructed by using palmprint-image databases, and PCA-based palmprint-recognition systems with an eigenspace that is constructed using a face-image database, the recognition rates are unexpectedly improved compared to the classic approach.