Automatic Fingerprint Classification Using Deep Learning Technology (DeepFKTNet)

Fahman Saeed, Muhammad Hussain and Hatim A. Aboalsamh
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Fahman Saeed: Department of Computer Science, King Saud University, Riyadh 11451, Saudi Arabia
Muhammad Hussain: Department of Computer Science, King Saud University, Riyadh 11451, Saudi Arabia
Hatim A. Aboalsamh: Department of Computer Science, King Saud University, Riyadh 11451, Saudi Arabia

Mathematics, 2022, vol. 10, issue 8, 1-17

Abstract: Fingerprints are gaining in popularity, and fingerprint datasets are becoming increasingly large. They are often captured utilizing a variety of sensors embedded in smart devices such as mobile phones and personal computers. One of the primary issues with fingerprint recognition systems is their high processing complexity, which is exacerbated when they are gathered using several sensors. One way to address this issue is to categorize fingerprints in a database to condense the search space. Deep learning is effective in designing robust fingerprint classification methods. However, designing the architecture of a CNN model is a laborious and time-consuming task. We proposed a technique for automatically determining the architecture of a CNN model adaptive to fingerprint classification; it automatically determines the number of filters and the layers using Fukunaga–Koontz transform and the ratio of the between-class scatter to within-class scatter. It helps to design lightweight CNN models, which are efficient and speed up the fingerprint recognition process. The method was evaluated two public-domain benchmark datasets FingerPass and FVC2004 benchmark datasets, which contain noisy, low-quality fingerprints obtained using live scan devices and cross-sensor fingerprints. The designed models outperform the well-known pre-trained models and the state-of-the-art fingerprint classification techniques.

Keywords: multisensory fingerprint; interoperability; DeepFKTNet; deep learning; classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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