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Proposing a proper method for face recognition is still a challenging subject in biometric and computer vision applications. Although some reliable systems were introduced under relatively controlled conditions, their recognition rate is not satisfactory in the general settings. This is especially true when there are variations in pose, illumination, and facial expression. To alleviate these problems,...
In this paper, we propose a pedestrian analysis solution helpful for adaptive content delivery and interest measurement for outdoor advertisement displays. The proposed system has built-in camera on the top panel of such displays which capture the real time viewers' frames. The captured frames have been analyzed for detection of faces using Viola-Jones algorithm. The detected faces have been processed...
Visual processing in humans is, without a doubt, far superior that that in machines, especially when the end goal is object or face recognition. Neural results from visual object and face recognition in humans provide an excellent model for developing better techniques in machine vision. In this study, we present a particular neural result pertaining to the use of low spatial frequency (LSF) imagery...
This paper presents a method to improve generalization capabilities of supervised neural networks based on topological data mapping used in Counter Propagation Networks (CPNs). Using topological data mapping on CPNs the method presented herein provides advantages to interpolate new data in sparse areas that exist among categories and to remove overlapping or conflicting data in original training data...
An algorithm for evolving neural network via the genetic algorithm based on GPU parallel architecture was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary) and was used on gender face recognition. By using the powerful ability of GPU parallel computing, CuParcone achieves a performance increase about 323 times than Parcone algorithm,...
The techniques of eigenfaces and neural net-based algorithms (LS-SVM and BP NNs) are combined to categorize gender from facial images in this paper. Based on exploration of the related techniques, the eigenfaces were firstly established from the training images, and the projection coefficients for training and testing images obtained in the space spanned by the eigenfaces; after that the LS-SVM and...
In this paper, it is proposed a facial biometric identification system, using discriminative common vector. This method reduces the number of characteristics of the different images from the database and selects the most discriminative of them. In this work, transformed domains, such as discrete cosine transformed (DCT), discrete wavelets transformed (DWT), principal component analysis (PCA), linear...
In this paper, a novel method is proposed for face recognition based on combined Gabor features and unit-linking pulse coupled neural network (PCNN) time signature. In this approach, a probe face is first divided into a 2times2 block then the mean and standard deviation of the Gabor sub-face image are extracted which includes 40-dimension features in each block. The PCNN time signature of a face image...
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...
In this paper, a novel method is proposed for face recognition based on pulse coupled neural network (PCNN) time signature. In this approach, a probe face is first extracted PCNN time signature as the recognition features, which a two-dimensional image is projected to a low one-dimensional feature space and then is classified based on the known samples. An extensive experimental investigation is conducted...
This paper presents a new artificial neural network, called I-PyraNet. This new architecture is based on the combination between concepts of the recently described PyraNet and the nonclassical receptive fields inhibition, integrating the feature extraction and the classification stages into the same structure which is formed by 2-D and 1-D layers. The main difference between the PyraNet and the I-PyraNet...
Support vector machines were developed in recent years, which have large advantage over the traditional neural network on small sample set for classification. In all research fields of these learning machines, the selection of kernel function is the most important problem, which has a closed relationship with the performance of classification. But the research work in this field is not enough. In...
This paper proposes an improved version of our previously introduced face detection system based on skin color segmentation and neural networks. The new system uses a support vector machine (SVM) based method for verification.
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