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In this paper, we propose a high performance face recognition system based on the probability distribution functions (PDF) extracted from discrete wavelet transform in different colour channels. The PDFs of the equalized and segmented faces in different subband images obtained from discrete wavelet transform (DWT) are used as statistical feature vectors for the recognition of faces by minimizing the...
This paper introduces a new face recognition method based on the gray-level co-occurrence matrix (GLCM). Both distributions of the intensities and information about relative position of neighbourhood pixels are carried by GLCM. Two methods have been used to extract feature vectors from the GLCM for face classification. The first, method extracts the well-known Haralick features to form the feature...
In this paper a new system for identifying faces from video sequences using adaptive and fixed eigenspace approaches with a novel fitness measure is proposed. During the recognition process, each image in the gallery set is assigned a fitness value. The fitness value is updated for each frame and at the end of the probe video; the person corresponding to the gallery image with the highest fitness...
In this paper, the problem of person-independent facial expression recognition from 3D facial features is investigated. We propose a methodology for the selection of features that uses a multi-objective genetic algorithm where the number of features is optimized to improve classification accuracy. The facial feature selection aims to derive a set of features from the original expression images, which...
This paper introduces a face recognition method based on the Dual-Tree Complex Wavelet Transform (DT-CWT), which is used to extract features from face images. DT-CWT uses similar kernels with Gabor wavelets and is a computationally cheaper way of extracting Gabor-like features. Principal Component Analysis (PCA) which is a linear dimensionality reduction technique, that attempts to represent data...
This paper proposes a high performance iris recognition system based on the probability distribution functions (PDF) of pixels in different colour channels. The PDFs of the segmented iris images are used as statistical feature vectors for the recognition of irises by minimizing the Kullback-Leibler distance (KLD) between the PDF of a given iris and the PDFs of irises in the training set. Feature vector...
In this paper, a novel image equalization technique which is based on singular value decomposition (SVD) is proposed. The singular value matrix represents the intensity information of the given image and any change on the singular values change the intensity of the input image. The proposed technique converts the image into the SVD domain and after normalizing the singular value matrix it reconstructs...
This paper proposes a novel iris recognition system based on matching colour pixel statistics. The proposed system uses colour histograms as pixel statistic feature vectors for recognition of irises by cross correlation between the histogram of a given iris and the histograms of irises in the database. Majority voting (MV) has been applied to achieve the final recognition. Iris images taken from the...
This paper describes a pose invariant three-dimensional (3D) facial expression recognition method using distance vectors retrieved from 3D distributions of facial feature points to classify universal facial expressions. Probabilistic Neural Network architecture is employed as a classifier to recognize the facial expressions from a distance vector obtained from 3D facial feature locations. Facial expressions...
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