Features extraction in images on finger veins with hybrid curves

Vasquez, Yazmin; Beltrán, Carlos E.; Gómez, Marisol; Flórez, Marco; Vazquez-Gonzalez, Jose Luis

doi:10.1109/mhtc.2017.7926196

Cited by 4 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…iris and eye movement) [72]. [73], [74], [75], [76], [77], [78], [79], [80] Behavioral (Gait, Signature, Handwriting) Sparse reconstruction based metric learning, Gradient local binary patterns, longest run feature [81], [82] Bioacoustics Pitch, Zernike moment [83], [84] Bio-Signal (EEG, ERG, ECG, EMG, EOG, GSR, MEG, MCG, MMG)…”

Section: )mentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract