Completely Contactless Finger-Knuckle Recognition using Gabor Initialized Siamese Network
| Autor: | Anuj Sharma, Rajiv Kapoor, Harshit, Dinesh Kumar, Anmol Garg |
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| Rok vydání: | 2020 |
| Předmět: |
Modality (human–computer interaction)
Biometrics Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Index finger Fingerprint recognition medicine.anatomical_structure Knuckle Gabor filter Rate of convergence medicine Computer vision Artificial intelligence business |
| Zdroj: | 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). |
| DOI: | 10.1109/icesc48915.2020.9155554 |
| Popis: | This document presents a novel approach for contactless finger-knuckle biometric modality using Gabor initialized Deep Siamese Network. For feature extraction of finger-knuckle creases, Gabor filter is used in convolutional layer of twin CNN of Siamese Network. Validation of the model uses N-way One S hot Learning technique. A database of 146 different subjects was recorded using a smartphone camera. It contains 5 different dorsal finger-knuckle images of right-hand index finger of each individual. Experimental results show an accuracy of 94.6% and a fast convergence rate of model, which illustrate the ease of use of finger-knuckle biometrics in online applications, specifically involving smartphones, laptops and other real-time systems involving biometric verifications. |
| Databáze: | OpenAIRE |
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