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Patch-based face recognition is a recent method which uses the idea of analyzing face images locally, in order to reduce the effects of illumination changes and partial occlusions. Feature fusion and decision fusion are two distinct ways to make use of the extracted local features. Apart from the well-known decision fusion methods, a novel approach for calculating weights for the weighted sum rule...
In this paper, we propose a novel biometric hashing method. We employ a password-generated random projection matrix applied to the face images directly instead of applying to the features extracted from face images and improve the methods in the literature. We aim to preserve privacy while achieving desirable accuracy in a biometric verification system. We do the verification in the hash domain and...
A facial feature extraction method is proposed in this work, which uses location and texture information given a face image. Location and texture information can automatically be learnt by the system, from a training data. Best facial feature locations are found by maximizing the joint distribution of location and texture information of facial features. Performance of the method was found promising...
Local appearance-based methods have been proposed recently for face recognition. We analyze the effects of different dimension reduction and normalization methods on local appearance-based face recognition in this paper. Each image is divided into equal sized blocks and six different dimension reduction methods are implemented for each block separately to create local visual feature vectors. On these...
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