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As per available reports about 6 relevant Journals and more than 10 upcoming Conferences on Biometrics Devices and about 197 articles have been published on Biometrics Devices.
Biometrics refers to metrics related to human characteristics and traits. Biometrics authentication (or realistic authentication) is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric devices are based on the two types of systems: 1) Multimodal Biometrics System 2) Adaptive Biometrics System. Multimodal biometric systems use multiple sensors or biometrics to overcome the limitations of unimodal biometric systems. For instance iris recognition systems can be compromised by aging irides and finger scanning systems by worn-out or cut fingerprints. While unimodal biometric systems are limited by the integrity of their identifier, it is unlikely that several unimodal systems will suffer from identical limitations. Multimodal biometric systems can obtain sets of information from the same marker (i.e., multiple images of an iris, or scans of the same finger) or information from different biometrics (requiring fingerprint scans and, using voice recognition, a spoken pass-code). Multimodal biometric systems can integrate these unimodal systems sequentially, simultaneously, a combination thereof, or in series, which refer to sequential, parallel, hierarchical and serial integration modes, respectively. The interested reader is pointed to Choubisa for detailed tradeoffs of response time, accuracy, and costs between integration modes. Broadly, the information fusion is divided into three parts, pre-mapping fusion, midst-mapping fusion, and post-mapping fusion/late fusion. In pre-mapping fusion information can be combined at sensor level or feature level. Sensor-level fusion can be mainly organized in three classes: (1) single sensor-multiple instances, (2) intra-class multiple sensors, and (3) inter-class multiple sensors. Feature-level fusion can be mainly organized in two categories: (1) intra-class and (2) inter-class. Intra-class is again classified into four subcategories: (a) Same sensor-same features, (b) Same sensor-different features, (c) Different sensors-same features, and (d) Different sensors-different features.
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Scope and Importance:
Adaptive biometric Systems aim to auto-update the templates or model to the intra-class variation of the operational data. The two-fold advantages of these systems are solving the problem of limited training data and tracking the temporal variations of the input data through adaptation. Recently, adaptive biometrics has received a significant attention from the research community. This research direction is expected to gain momentum because of their key promulgated advantages. First, with an adaptive biometric system, one no longer needs to collect a large number of biometric samples during the enrollment process. Second, it is no longer necessary to re-enroll or retrain the system from scratch in order to cope with the changing environment. This convenience can significantly reduce the cost of maintaining a biometric system. Despite these advantages, there are several open issues involved with these systems. For mis-classification error (false acceptance) by the biometric system, cause adaptation using impostor sample.
Market Analysis:
The overall biometric market is increasing with a CAGR of 17.6% from 2014 to 2020. The major applications for the biometric market are travel and immigration and government. Fingerprint biometric is the major shareholder in the biometric market and voice has the highest growth potential, followed by iris recognition; the market is currently valued at approximately $5 billion; fingerprint identification technology may see the biggest gains growing upto $6 billion in 2015; the market for face, iris, vein, and voice recognition is expected to expand upto $3.5 billion; large government ID and security programs are key drivers in fueling growth. Reports project that the global biometrics market is expected to grow upto $12 billion in 2015 from its current estimated value of $5 billion. As per the top reports, estimated annual growth rate is upto 18.9 percent.
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This page was last updated on April 25, 2024