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Pattern mining approaches used in sensor-based biometric recognition: a review

Chaki, J., Dey, N., Shi, F. and Sherratt, R. S. (2019) Pattern mining approaches used in sensor-based biometric recognition: a review. IEEE Sensors Journal, 19 (10). pp. 3569-3580. ISSN 1530-437X

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To link to this item DOI: 10.1109/JSEN.2019.2894972

Abstract/Summary

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Biomedical Sciences
Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:81880
Publisher:IEEE

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