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In this paper, we propose a very simple face recognition method. This method first exploits a linear combination of all the training samples to express the test sample. Then it evaluates the capability of each class in expressing the test sample and assigns the test sample to the class that has the strongest capability. Using the expression result, the proposed method can classify the testing sample...
In this work, we present a bimodal biometric system using speech and face features and tested its performance under degraded condition. Speaker verification (SV) system is built using Mel-Frequency Cepstral Coefficients (MFCC) followed by delta and delta-delta for feature extraction and Gaussian Mixture Model (GMM) for modeling. A face verification (FV) system is built using the combination of Principal...
A client specific linear discriminant analysis (CSLDA) based face authentication system has been developed with multi-level fuzzy score fusion. The CSLDA method provides two measures for authentication: distance to the client (Client Score) and distance to the mean of impostors (Impostor Score). A two-level multi-sample score fusion method has been proposed. A fuzzy inference module has also been...
In this paper we propose a set of biometric recognition experiments in similar conditions to real operating systems. This implies a jump from the usual laboratory conditions to a more real situation where the amount of variability between training and testing samples is large. We present experiments with face and hand-geometry recognition training a ldquouniversal classifierrdquo able to decide if...
A general framework of fusion at decision level, which works on ROCs instead of matching scores, is investigated. Under this framework, we further propose a hybrid fusion method, which combines the score-level and decision-level fusions, taking advantage of both fusion modes. The hybrid fusion adaptively tunes itself between the two levels of fusion, and improves the final performance over the original...
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