Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review

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.