Efficient personal identification using multimodal biometrics

Abstract

For recent years, the use of personal identity systems using multimodal biometrics has been increasing tremendously in number of fields. Although the Unimodal biometric systems serve well in various areas, it is notable that they have disadvantages regarding security and accuracy. Multimodal biometric systems focus on combining more than one possible biometric technology in order to secure the applications to a great extent. This in turn resulted in the improvement of performance and robustness against fraudulent attacks. Personal identification plays a major role in any information sharing process. For this, the identification systems must be designed in such a manner that it should minimize the system error rates, susceptibility mimics and false match rate. Considering these factors, we aim at providing a new personal identification system using multimodal biometrics. The proposed system uses three of the biometric technologies in the process of identification: (i) Face recognition; (ii) Fingerprint recognition; and (iii) Speech Recognition. Here we discuss about the methods of feature extraction, fusion and decision used in our system and their advantages over other biometric systems.

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration

“…In biometrics, modalities can be classified into four categories based on their characteristics [18,19]. The first category is image-based, which includes biometrics such as fingerprints, iris scans, facial recognition, and hand geometry [20].…”