Multimodal biometric authentication

In the present era of information technology, there is a need to implement authentication and authorization techniques for security of resources. There are number of ways to prove authentication and authorization. But the biometric authentication beats all other techniques. Biometric techniques prove the authenticity or authorization of a human being based on his/her physiological or behavioural traits. Biometrics is a technique by which an individual's identity can be authenticated by applying the physical or behavioural trait. Physical traits like fingerprints, palm, iris etc. are based on the physical characteristics which are generally inherent and unique. Behavioural traits like voice, signature or keystroke dynamics etc. on the other hand, are quantifiable characteristics.They also protect access of resources from unauthorized users. Multimodal biometrics refers to the use of a combination of two or more biometric modalities in a verification / identification system. Identification based on multiple biometrics represents an emerging trend. The most compelling reason to combine different modalities is to improve the recognition rate. This can be done when biometric features of different biometrics are statistically independent. A multimodal biometric identification system aims to fuse two or more physical or behavioural traits. Multimodal biometric system is used in order to improve the accuracy. Multimodal biometric identification system based on iris, palm and fingerprint trait based on fusion logic is proposed. Typically in a multimodal biometric system, each biometric trait processes its information independently. The processed information is combined using curve let transform

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

MobiBits: Multimodal Mobile Biometric Database

This paper presents a novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device. In addition to this collection of data we perform an extensive set of experiments providing insight on benchmark recognition performance that can be achieved with these data, carried out with existing commercial and academic biometric solutions. This is the first known to us mobile biometric database introducing samples of biometric traits such as thermal hand images and thermal face images. We hope that this contribution will make a valuable addition to the already existing databases and enable new experiments and studies in the field of mobile authentication. The MobiBits database is made publicly available to the research community at no cost for non-commercial purposes.Comment: Submitted for the BIOSIG2018 conference on June 18, 2018. Accepted for publication on July 20, 201

An Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image

Biometrics based personal identification is regarded as an effective method for automatically recognizing, with a high confidence a person’s identity. A multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically better recognition performance compare to system based on a single biometric modality. This paper proposes an authentication method for a multimodal biometric system identification using two traits i.e. face and palmprint. The proposed system is designed for application where the training data contains a face and palmprint. Integrating the palmprint and face features increases robustness of the person authentication. The final decision is made by fusion at matching score level architecture in which features vectors are created independently for query measures and are then compared to the enrolment template, which are stored during database preparation. Multimodal biometric system is developed through fusion of face and palmprint recognition