Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
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
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Flexible and Efficient Hardware Architecture for Real-Time Face Recognition Based on Eigenface
ISVLSI '05 Proceedings of the IEEE Computer Society Annual Symposium on VLSI: New Frontiers in VLSI Design
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Design and implementation of a fast parallel architecture based on an improved principal component analysis (PCA) method called Composite PCA suitable for real-time face recognition is presented in this paper. The proposed architecture performs the tasks of both feature extraction and classification. Composite PCA takes in to consideration the local features of face images, which do not vary widely between face images of the same person taken under varying expression, illumination and pose. Hence it leads to a better recognition rate than PCA. Composite PCA has more parallelism than conventional PCA and this parallelism is utilized to design an efficient architecture capable of performing real-time face recognition. The face recognition system is implemented in an FPGA environment and tested using standard databases. The system is able to recognize a person from a database of 110 images of 10 individuals in approximately 4 ms.