Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Face Recognition System for Real Time Surveillance
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
A Biometric Identification System Based on Eigenpalm and Eigenfinger Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast approach for dimensionality reduction with image data
Pattern Recognition
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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We present a feature partitioning framework for principal component analysis (PCA) on image data. Using this framework, we propose two novel methods, sub-image principal component analysis (SIMPCA) and flexible image principal component analysis (FLPCA). We prove the computational superiority of the approaches and also demonstrate improved performance through experimentation on standard face databases and a palmprint database. The proposed methods show a significantly superior performance as compared to conventional and improved implementations of PCA on images. It is seen that improvement in performance is in terms of both computational time and recognition rate. Experimentation shows that the novel partitioning approaches are in a different class of approaches. The success of proposed approaches may be attributed to the localization effect derived from partitioning. The proposed methods use a more appropriate matrix representation of the image data.