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THIS PAPER PRESENTS AND DETAILS THE INVESTIGATIONS INTO THE EFFECTIVENESS OF VARIOUS SCORE LEVEL FUSION APPROACHES IN BOTH MULTI-SAMPLE AND MULTIMODAL BIOMETRIC SYSTEMS. EXPERIMENTAL INVESTIGATIONS ARE CONDUCTED USING FACE AND SPEECH MODALITIES. THE SCORES FOR FACE AND VOICE BIOMETRICS ARE BASED ON THE USE OF DIFFERENT FEATURES EXTRACTED FROM THE XM2VTS DATABASE. THESE SCORES ARE PROVIDED BY IDIAP...
There is mounting evidence about the benefit of tailoring a biometric authentication system to each user by postprocessing the system output at the score level, also known as client-specific score normalisation. Examples of these procedures are Z-norm and F-norm. These procedures can calibrate the uneven hypothesis space such that the dispropotionate false acceptance and false rejection errors are...
Lip region deformation during speech contains biometric information and is termed visual speech. This biometric information can be interpreted as being genetic or behavioral depending on whether static or dynamic features are extracted. In this paper, we use a texture descriptor called local ordinal contrast pattern (LOCP) with a dynamic texture representation called three orthogonal planes to represent...
The lip-region can be interpreted as either a genetic or behavioural biometric trait depending on whether static or dynamic information is used. In this paper, we use a texture descriptor called Local Ordinal Contrast Pattern (LOCP) in conjunction with a novel spatiotemporal sampling method called Windowed Three Orthogonal Planes (WTOP) to represent both appearance and dynamics features observed in...
In this paper, an algorithm for person verification is proposed. Dempster-Shafer Theory is used for face and voice fusion at the score level in order to overcome the limitations of unimodal biometric systems. Our experiments on the publicly available scores of the XM2VTS Benchmark database show a consistent improvement in performance compared to each individual modality. We have compared our approach...
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...
We propose speaker gender recognition achieved by using score level fusion by AdaBoost. Soft biometrics has been focused on because recognition by fusing biometric systems and soft biometric traits may improve the accuracy of recognition and decrease the time for this. Gender recognition is important for speaker recognition and can provide important information to speaker recognition systems. Mel-frequency...
Information fusion in biometrics has received considerable attention. The architecture proposed here is based on the sequential integration of multi-instance and multi-sample fusion schemes. This method is analytically shown to improve the performance and allow a controlled trade-off between false alarms and false rejects when the classifier decisions are statistically independent. Equations developed...
Using local features generally provides higher accuracies compared to a global feature vector in face identification. In this study, taking into account the fact that better multimodal systems generally include individually good experts, multimodal identification using speech and local feature based face experts is studied. Both spPCA and mPCA are considered for this purpose. Experiments on XM2VTS...
Multimodal biometrics has drawn lot of attention in recent days as it provides more reliable scheme for person verification. Multimodal biometrics includes the fusion of information from different modalities. This paper presents a novel method for assigning weights before performing fusion at match score level. The proposed method is based on False Acceptance Rate (FAR) and Genuine Acceptance Rate...
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