Research Area:  Machine Learning
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 the 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.
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Author(s) Name:   Jyotismita Chaki; Nilanjan Dey; Fuqian Shi; R. Simon Sherratt
Journal name:  IEEE Sensors Journal
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Publisher name:  IEEE
DOI:  10.1109/JSEN.2019.2894972
Volume Information:  Volume: 19, Issue: 10, 15 May 2019, Page(s): 3569 - 3580
Paper Link:   https://ieeexplore.ieee.org/abstract/document/8625438