Fingerprint identification using graph matching☆
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Cited by (136)
EEG-based user identification system using 1D-convolutional long short-term memory neural networks
2019, Expert Systems with ApplicationsCitation Excerpt :A biometric system is often designed to extract features by applying signal processing, machine learning and pattern recognition techniques on the user’s physiological signals, and compare the features with the users’ profiles/templates stored in the database. Physiological and behavioral biometric traits, such as fingerprint (Isenor & Zaky, 1986), face (Samaria & Harter, 1994), gait (Sun & Lo, 2018), and electrocardiography (ECG) (Zhao, Yang, Chen, & Luo, 2013) have been widely accepted and applied in user identification systems. Despite the popularity of such biometric systems, there are weaknesses in using fingerprint, iris or voice for user identification.
A fast projected fixed-point algorithm for large graph matching
2016, Pattern RecognitionCitation Excerpt :Graph matching, aiming to find the optimal correspondences between the nodes of two graphs, is an important and active topic of research in computer vision and pattern recognition [1,2]. It has been extensively applied in various fields including optical character recognition [3,4], object recognition [5,6], shape matching [6–8], face recognition [9], feature correspondence [10], point matching [11], image retrieval [12], video indexing [13], document processing [14], protein classification [15] and fingerprint identification [16]. Graph matching is in general a NP-hard discrete optimization problem.
MLDA: Multi-Loss Domain Adaptor for Cross-Session and Cross-Emotion EEG-Based Individual Identification
2023, IEEE Journal of Biomedical and Health InformaticsDM-EEGID: EEG-Based Biometric Authentication System Using Hybrid Attention-Based LSTM and MLP Algorithm
2023, Traitement du SignalHandbook of fingerprint recognition: Third edition
2022, Handbook of Fingerprint Recognition: Third EditionMagnetic composite based on cellulose and GO for latent fingerprint visualization
2022, Egyptian Journal of Chemistry
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This work was partially supported by the Natural Sciences and Engineering Research Council of Canada, under grant No. A8994.