Contactless Fingerprint Recognition Using Deep Learning—A Systematic Review
Contactless fingerprint identification systems have been introduced to address the deficiencies of contact-based fingerprint systems. A number of studies have been reported regarding contactless fingerprint processing, including classical image processing, the machine-learning pipeline, and a number...
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| Published in | Journal of cybersecurity and privacy Vol. 2; no. 3; pp. 714 - 730 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Washington
MDPI AG
08.09.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2624-800X 2624-800X |
| DOI | 10.3390/jcp2030036 |
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| Abstract | Contactless fingerprint identification systems have been introduced to address the deficiencies of contact-based fingerprint systems. A number of studies have been reported regarding contactless fingerprint processing, including classical image processing, the machine-learning pipeline, and a number of deep-learning-based algorithms. The deep-learning-based methods were reported to have higher accuracies than their counterparts. This study was thus motivated to present a systematic review of these successes and the reported limitations. Three methods were researched for this review: (i) the finger photo capture method and corresponding image sensors, (ii) the classical preprocessing method to prepare a finger image for a recognition task, and (iii) the deep-learning approach for contactless fingerprint recognition. Eight scientific articles were identified that matched all inclusion and exclusion criteria. Based on inferences from this review, we have discussed how deep learning methods could benefit the field of biometrics and the potential gaps that deep-learning approaches need to address for real-world biometric applications. |
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| AbstractList | Contactless fingerprint identification systems have been introduced to address the deficiencies of contact-based fingerprint systems. A number of studies have been reported regarding contactless fingerprint processing, including classical image processing, the machine-learning pipeline, and a number of deep-learning-based algorithms. The deep-learning-based methods were reported to have higher accuracies than their counterparts. This study was thus motivated to present a systematic review of these successes and the reported limitations. Three methods were researched for this review: (i) the finger photo capture method and corresponding image sensors, (ii) the classical preprocessing method to prepare a finger image for a recognition task, and (iii) the deep-learning approach for contactless fingerprint recognition. Eight scientific articles were identified that matched all inclusion and exclusion criteria. Based on inferences from this review, we have discussed how deep learning methods could benefit the field of biometrics and the potential gaps that deep-learning approaches need to address for real-world biometric applications. |
| Author | Chowdhury, A M Mahmud Imtiaz, Masudul Haider |
| Author_xml | – sequence: 1 givenname: A M Mahmud surname: Chowdhury fullname: Chowdhury, A M Mahmud – sequence: 2 givenname: Masudul Haider surname: Imtiaz fullname: Imtiaz, Masudul Haider |
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| SubjectTerms | Algorithms Biometric identification Biometrics contactless fingerprint Deep learning Digital cameras fingerprint analysis fingerprint recognition Fingerprinting Machine learning Neural networks Performance evaluation Sensors Smartphones Systematic review |
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| Title | Contactless Fingerprint Recognition Using Deep Learning—A Systematic Review |
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