Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast algorithm for tracking human faces based on chromatic histograms
Pattern Recognition Letters
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face authentication using adapted local binary pattern histograms
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A Dynamic Histogram Equalization for Image Contrast Enhancement
IEEE Transactions on Consumer Electronics
Face segmentation using skin-color map in videophone applications
IEEE Transactions on Circuits and Systems for Video Technology
Clustering of human motions based on feature-level fusion of multiple body sensor data
Proceedings of the 1st ACM International Health Informatics Symposium
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A new and high performance face recognition system based on combining the decision obtained from the probability distribution functions (PDFs) of pixels in different colour channels is proposed. The PDFs of the equalized and segmented face images are used as statistical feature vectors for the recognition of faces by minimizing the Kullback-Leibler Divergence (KLD) between the PDF of a given face and the PDFs of faces in the database. Many data fusion techniques such as median rule, sum rule, max rule, product rule, and majority voting and also feature vector fusion as a source fusion technique have been employed to improve the recognition performance. The proposed system has been tested on the FERET, the Head Pose, the Essex University, and the Georgia Tech University face databases. The superiority of the proposed system has been shown by comparing it with the state-of-art face recognition systems.