Algorithms for clustering data
Algorithms for clustering data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Cluster validity methods: part I
ACM SIGMOD Record
Towards Automatic Face Identification Robust to Ageing Variation
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Two-stage approach for pose invariant face recognition
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Illuminating light field: image-based face recognition across illuminations and poses
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A learning based self-organized additive fuzzy clustering method and its application for EEG data
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
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Though research on face recognition has been carried out for a decade, it has in trouble with different situations i.e. facial expression, view point, illumination conditions, noise, etc. To solve this problem, we propose to define situation specific actions for face recognition in this paper. The proposed system partitions face images into several image contexts (groups) based on cluster validity, and takes adaptation to individual partitioned groups. As there is no formal way to select whether the clustering algorithm is suitable or not, we propose a new adaptive cluster validity approach in comparison with Dunn's cluster validity measurement. After selecting proper image context, we train using genetic algorithm to individual feature elements generated by Gabor wavelet of a face image to produce weights. In Gabor wavelet based face recognition, we apply weights to individual elements of facial feature, and those weights are trained by Genetic algorithm. These weights are applied for classifying face images during face recognition time. We applied this concept to face recognition field in different situations and we achieved encouraging results in comparison with Dunn's measuring.