Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding
Computers & Mathematics with Applications
Improved Quantum Evolutionary Algorithm Combined with Chaos and Its Application
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
An adaptive quantum-based evolutionary algorithm for multiobjective optimization
WSEAS Transactions on Systems and Control
Multi-criterion decision making in distrbiuted systems by quantum evolutionary algorithms
ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
Optimizing surplus harmonics distribution in PWM
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
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Face verification is considered to be the main part of the face detection system. To detect human faces in images, face candidates are extracted and face verification is performed. This paper proposes a new face verification algorithm using Quantum-inspired Evolutionary Algorithm (QEA). The proposed verification system is based on Principal Components Analysis (PCA). Although PCA related algorithms have shown outstanding performance, the problem lies in the selection of eigenvectors. They may not be the optimal ones for representing the face features. Moreover, a threshold value should be selected properly considering the verification rate and false alarm rate. To solve these problems, QEA is employed to find out the optimal distance measure under the predetermined threshold value which distinguishes between face images and non-face images. The proposed verification system is tested on the AR face database and the results are compared with the previous works to show the improvement in performance.