Probabilistic Visual Learning for Object Representation
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Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computational model for visual selection
Neural Computation
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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International Journal of Computer Vision
Feature selection in scientific applications
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A System Identification Approach for Video-based Face Recognition
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Real-Time Face Verification System on a Cell-Phone Using Advanced Correlation Filters
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
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Improving Face Recognition by Online Image Alignment
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A Component-based Framework for Face Detection and Identification
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Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
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ITCS '09 Proceedings of the 2009 International Conference on Information Technology and Computer Science - Volume 01
New Image Fusion-Based Algorithm to Face Recognition
MUE '09 Proceedings of the 2009 Third International Conference on Multimedia and Ubiquitous Engineering
Component-based LDA face description for image retrieval and MPEG-7 standardisation
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CGIM '07 Proceedings of the Ninth IASTED International Conference on Computer Graphics and Imaging
Improved face representation by nonuniform multilevel selection of Gabor convolution features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
Local facial asymmetry for expression classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
CICN '10 Proceedings of the 2010 International Conference on Computational Intelligence and Communication Networks
Components for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Using component features for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Built-in face recognition for smart photo sharing in mobile devices
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
GA-fisher: a new LDA-based face recognition algorithm with selection of principal components
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Low-memory requirement and efficient face recognition system based on DCT pyramid
IEEE Transactions on Consumer Electronics
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A Comparative Study of Local Matching Approach for Face Recognition
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An introduction to biometric recognition
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Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
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Numerous face recognition techniques have been developed owing to the growing number of real-world applications. Most of current algorithms for face recognition involve considerable amount of computations and hence they cannot be used on devices constrained with limited speed and memory. In this paper, we propose a novel solution for efficient face recognition problem for systems that utilize small memory capacities and demand fast performance. The new technique divides the face images into components and finds the discriminant phases of the Fourier transform of these components automatically using the sequential floating forward search method. A thorough study and comprehensive experiments relating time consumption versus system performance are applied to benchmark face image databases. Finally, the proposed technique is compared with other known methods and evaluated through the recognition rate and the computational time, where we achieve a recognition rate of 98.5% with computational time of 6.4 minutes for a database consisting of 2360 images.