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This paper proposes a multimodal biometric system through Gaussian mixture model (GMM) for face and ear biometrics with belief fusion of the estimated scores characterized by Gabor responses and the proposed fusion is accomplished by Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gabor wavelet filters to extracts spatially enhanced Gabor facial features and Gabor ear...
Ear biometric is considered as one of the most reliable and invariant biometrics characteristics in line with iris and fingerprint characteristics. In many cases, ear biometrics can be compared with face biometrics regarding many physiological and texture characteristics. In this paper, a robust and efficient ear recognition system is presented, which uses Scale Invariant Feature Transform (SIFT)...
Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore decoupling the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. This paper presents a robust face recognition technique based on the extraction and matching of...
Multi-biometric systems have many advantages over the uni-biometric systems. However, multi-biometric systems lacking in many respects, such as multimodal systems not only acquire relevant and viable information for fusion, but also acquire some irrelevant and redundant information which are associated to the feature sets or with the match score sets, and this may lead to the resultant performance...
This paper presents a novel biometric sensor generated evidence fusion of face and palmprint images using wavelet decomposition for personnel identity verification. The approach of biometric image fusion at sensor level refers to a process that fuses multispectral images captured at different resolutions and by different biometric sensors to acquire richer and complementary information to produce...
This paper presents a novel face recognition technique with graph topology drawn on scale invariant feature transform (SIFT) features and is compared with all the available well known techniques on SIFT features, and elastic bunch graph matching (EBGM) technique drawn on gabor wavelet feature. IITK face database is used for evaluation purpose. Test results show that the proposed graph matching technique...
This paper proposes a procedure for facial template synthesis based on features extracted from multiple facial instances with varying pose. The proposed system extracts the rotation, scale and translation invariant SIFT features, also having high discrimination ability, from the frontal and half left and right profiles of an individual face images. These affine invariant features obviate the need...
This paper presents a new face identification system based on graph matching technique on SIFT features extracted from face images. Although SIFT features have been successfully used for general object detection and recognition, only recently they were applied to face recognition. This paper further investigates the performance of identification techniques based on Graph matching topology drawn on...
This paper proposes a robust feature level based fusion classifier for face and fingerprint biometrics. The proposed system fuses the two traits at feature extraction level by first making the feature sets compatible for concatenation and then reducing the feature sets to handle the 'problem of curse of dimensionality'; finally the concatenated feature vectors are matched. The system is tested on...
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