A robust multimodal biometric scheme for human recognition and authentication. N2 - Biometric recognition and authentication are crucial and gaining popularity in many security applications including secure access control, human surveillance, suspicious activity recognition, border monitoring, preventing criminal acts, alarm monitoring and so on. Biometric recognition identifies a human identity based upon their physiological or behavioral characteristics such as face, ear, fingerprint, palm print, iris, voice, gait and signature. Among these biometrics, the face and ear are considered as the most reliable traits due to their uniqueness and easy data acquisition. However, both face and ear recognition suffer from lack of accuracy and robustness for real time applications. The performance of face recognition process is significantly affected due to variations in facial expressions, the use of cosmetics and eye glasses, the presence of facial hair including beards and aging.
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Activity related biometrics for person authentication. One of the major challenges in human-machine interaction has always been the development of such techniques that are able to provide accurate human recognition, so as to other either personalized services or to protect critical infrastructures from unauthorized access. To this direction, a series of well stated and efficient methods have been proposed mainly based on biometric characteristics of the user. Despite the significant progress that has been achieved recently, there are still many open issues in the area, concerning not only the performance of the systems but also the intrusiveness of the collecting methods.
This Thesis is focused on the combination of multiple biometric traits for automatic person authentication, in what is called a multimodal biometric system. More generally, any type of biometric information can be combined in what is called a multibiometric system. The information sources in multibiometrics include not only multiple biometric traits but also multiple sensors, multiple biometric instances e. Most of the approaches found in the literature for combining these various information sources are based on the combination of the matching scores provided by individual systems built on the different biometric evidences. The combination schemes following this architecture are typically based on combination rules or trained pattern classifiers, and most of them assume that the score level fusion function is fixed at verification time.
PhD thesis, Concordia University. In recent years, biometric-based authentication systems have become very important in view of their ability to prevent identity theft by identifying an individual with high accuracy and reliability. Multimodal biometric systems have now drawn some attention in view of their ability to provide a performance superior to that provided by the corresponding unimodal biometric systems by utilizing more than one biometric modality. The existing multimodal biometric systems fuse multiple modalities at a single level, such as sensor, feature, score, rank or decision, and no study to fuse the modalities at more than one level that may lead to a further improvement in the performance of multimodal biometric systems, has been hitherto undertaken. In this thesis, multimodal biometric systems, wherein fusions of the modalities are carried out at more than one level, are investigated.