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In this paper we have proposed a new way to achieve the optimum learning rate that can reduce the learning time of the multi layer feed forward neural network. The effect of optimum numbers of inner iterations and numbers of hidden nodes on learning time and recognition rate has been shown. The Principal Component Analysis and Multilayer Feed Forward Neural Network are applied in face recognition...
In this paper, we propose a face detection framework that combines both feature, and skin pixel approaches, while making the framework self adaptive which is important for non controlled environmental conditions. The framework uses skin color information to reduce the search space for faces by localizing the probable skin regions using a mixture of multivariate Gaussians whose parameters are first...
This paper proposes a novel method for facial expression recognition based on neural network ensemble. The facial expression features are extracted firstly through multi expression eigenspace analysis, and then several neural networks are trained each with an eigenspace of different expressions respectively. At last their training results are aggregated as inputs of the ensemble classifier, which...
This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce the computational effort. The feature extraction procedure is carried out by PCA, and the results are compared to two other well known supervised learning classifiers; Artificial...
This paper presents work towards recognizing facial expressions that are used in sign language recognition. Facial features are tracked to effectively capture temporal visual cues on the signer's face during signing. A Bayesian framework is proposed as a feedback mechanism to the Kanade-Lucas-Tomasi (KLT) tracker for reliably tracking facial features in the presence of head motions and temporary occlusions...
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