Abstract
Fingerprints, as one of the most widely used biometric modalities, can be used to identify and distinguish between genders. Gender classification is very important in reducing the time when investigating criminal offenders and gender impersonation. In this work, we use deep Convolutional Neural Networks (CNNs) to not only classify fingerprints by gender, but also identify individual hands and fingers. Transfer learning is employed to speed up the training of the CNN. The CNN achieves an accuracy of 75.2%, 93.5%, and 76.72% for the classification of gender, hand, and fingers, respectively. These results obtained using our publicly available Sokoto Coventry Fingerprint Dataset (SOCOFing) serve as benchmark classification results on this dataset.
Original language | English |
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Title of host publication | Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1161-1165 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-5386-6805-4 |
ISBN (Print) | 978-1-5386-6806-1 |
DOIs | |
Publication status | Published - 17 Jan 2019 |
Event | 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 - Orlando, United States Duration: 17 Dec 2018 → 20 Dec 2018 |
Conference
Conference | 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 |
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Country/Territory | United States |
City | Orlando |
Period | 17/12/18 → 20/12/18 |
Keywords
- Convolutional Neural Networks
- Fingerprints
- Gender Classification
- Transfer Learning
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Safety, Risk, Reliability and Quality
- Signal Processing
- Decision Sciences (miscellaneous)