The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast and Accurate Face Detector Based on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: From Theory to Applications
Face Recognition: From Theory to Applications
Dynamic Vision: From Images to Face Recognition
Dynamic Vision: From Images to Face Recognition
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Neural Computation
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Fast Modular network implementation for support vector machines
IEEE Transactions on Neural Networks
Emotion recognition with consideration of facial expression and physiological signals
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Expert Systems with Applications: An International Journal
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In this paper, a support vector machine (SVM) based adaptive color switching for human face tracking is proposed. The color space is switching to the most appropriate color space model (CSM) according to circumstance conditions adaptively. Recently, many face tracking algorithms used empirical skin color model to discriminate skin/non-skin regions. These skin color models not consider illumination variation and result in less capacity to model skin color distribution. In this work, four color spaces and Laws texture extracted from face image database are used to train each SVM independently. In the pre-processing, the discrete wavelet transform (DWT) refines the face features would concentrate important features and reduce the computational complexity. Then, the features are transformed into four CSMs for SVMs which provide good generalization through optimal hyperplane. In testing, we perform quality measurement method to evaluate the face tracking performance and aggregating each SVM classification results to color space switching. Experimental results show that the proposed method would switch to the most appropriate color space according to quality measurement, automatically.