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Biometric systems allow automatic person recognition based on physical or behavioral features which belong to a certain person. Each biometric feature has its limits and no biometric system is perfect so unimodal biometric systems raise a variety of problems. To over fulfilling some of the mentioned inconvenient and limitations and to increase the level of security the multimodal biometric systems...
This paper presents an empirical investigation of two sparse random projections which correspond to extraction of vertical and horizontal features from a face image for identity verification. In order to enhance the performance of each projection, the matching scores of both directional features are fused via a total error rate minimization. The BERC face database is used for evaluating the effectiveness...
We propose in this paper a novel supervised manifold learning algorithm, called uncorrelated multilinear geometry preserving projections (UMGPP), incorporating both the Fisher criterion and manifold criterion to learn multiple interrelated subspaces in an iterative manner for efficient multimodal biometric recognition. In contrast to the existing GPP algorithm, UMGPP learns multiple feature subspaces...
Gait is a potential behavioral feature, and many allied studies have demonstrated that it can be served as a useful biometric feature for recognition. This paper described a novel gait recognition technique based on support vector machine fusion of contour projection and skeleton model features. A principal component analysis method was used to lower the dimension of contour projection after segmenting...
This paper introduces a framework that employs the Fisher linear discriminant model (FLDM) and classifier (FLDC) on integrated facial appearance and facial expression features. The principal component analysis (PCA) is firstly applied for dimensionality reduction. The normalized fusion method is then applied to the reduced lower dimensional subspaces of these two features. Finally, the FLDM is used...
Gait recognition, a new biometrics recognition technology, can discriminate individuals by the way they walk. A novel gait recognition method based on standard deviation energy image is proposed in this paper. Firstly, it divides video sequences into several gait cycles. Secondly, two kinds of energy images called nonzero and zero standard deviation energy image respectively are constructed. Finally,...
A score-based fusion for face verification is presented from FRAV3D face database (2D, 2.5D and 3D face images). In the case of 2.5D and 3D data, an automatic correction of pose has been carried out by detecting the nose tip and the eyes. For each kind of image a different feature extraction has been applied (principal component analysis and support vector machine for 2D and 2.5D, and iterative closest...
In this paper, we implement a face-hashing algorithm based on feature fusion and Gabor feature extraction, using conditional mutual information. The proposed method comprises three components: feature extraction, feature discretization and key generation. During the feature extraction stage, global features (PCA-transformed), local features, and a set of informative and non-redundant Gabor features...
We present a novel approach to generate cryptographic keys from biometric face data so that their privacy and biometric template can be protected by using helper data schema (HDS). Our method includes three components: feature extraction, feature discretization and key generation. During feature extraction stage, the global features (PCA-transformed) and local features (Gabor wavelet-transformed)...
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