Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Component-based LDA Method for Face Recognition with One Training Sample
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Handbook of Face Recognition
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Face recognition by fractal transformations
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
2D and 3D face recognition: A survey
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Likelihood Ratio-Based Biometric Score Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
View invariant head recognition by Hybrid PCA based reconstruction
Integrated Computer-Aided Engineering
Score normalization in multimodal biometric systems
Pattern Recognition
Fusion of possibilistic sources of evidences for pattern recognition
Integrated Computer-Aided Engineering
Face recognition/detection by probabilistic decision-based neural network
IEEE Transactions on Neural Networks
Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
Identification of anatomic retinal structures for macular delineation in fluorescein angiograms
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering
2D and 3D palmprint information, PCA and HMM for an improved person recognition performance
Integrated Computer-Aided Engineering
An adaptive regularization method for sparse representation
Integrated Computer-Aided Engineering
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Face recognition has a large number of applications, including security/counterterrorism, person identification, Internet communications, E-commerce, and computer entertainment. Although research in automatic face recognition has been conducted since the 1960s, there exist research challenges in its practical application in the terms of performance accuracy, which deteriorates significantly with changes in illumination, pose, expression and occlusions. However, these inherent limitations can be potentially alleviated by fusing biometric information based on multiple facial features. Following this vision, the work presented here offers three contributions. Firstly, we present a Face Recognition System, where diverse biometrics features such as total face, eyes, nose, mouth, etc are extracted from the face image. Secondly, we analyse a number of approaches for combining the aforementioned information at matching score level. Thirdly, we proposed a new approach, based on a recently proposed optimisation technique, the Bees Algorithm, to determine the optimal weight parameters to enhance the performance of the fusion system. Experiments on the CASIA and ORL face databases indicate that the proposed method achieves consistently high recognition rates, compared to traditional FR approaches, such as the Eigenfaces, Fisherfaces, and D-LDA methods.