Open Access

ARTICLE

Systematic Analysis of Latent Fingerprint Patterns through Fractionally Optimized CNN Model for Interpretable Multi-Output Identification

Mubeen Sabir¹, Zeshan Aslam Khan^2,*, Muhammad Waqar², Khizer Mehmood¹, Muhammad Junaid Ali Asif Raja³, Naveed Ishtiaq Chaudhary⁴, Khalid Mehmood Cheema⁵, Muhammad Asif Zahoor Raja⁴, Muhammad Farhan Khan⁶, Syed Sohail Ahmed⁷

1 Department of Electrical and Computer Engineering, International Islamic University, Islamabad, 44000, Pakistan
2 International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan
3 Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan
4 Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan
5 Department of Electronic Engineering, Fatima Jinnah Women University, Rawalpindi, 46000, Pakistan
6 College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China
7 Department of Computer Engineering, College of Computer, Qassim University, Al-Qassim, 51452, Saudi Arabia

* Corresponding Author: Zeshan Aslam Khan. Email:

(This article belongs to the Special Issue: Innovative Applications of Fractional Modeling and AI for Real-World Problems)

Computer Modeling in Engineering & Sciences 2025, 145(1), 807-855. https://doi.org/10.32604/cmes.2025.068131

Received 21 May 2025; Accepted 06 August 2025; Issue published 30 October 2025

Abstract

Fingerprint classification is a biometric method for crime prevention. For the successful completion of various tasks, such as official attendance, banking transactions, and membership requirements, fingerprint classification methods require improvement in terms of accuracy, speed, and the interpretability of non-linear demographic features. Researchers have introduced several CNN-based fingerprint classification models with improved accuracy, but these models often lack effective feature extraction mechanisms and complex multineural architectures. In addition, existing literature primarily focuses on gender classification rather than accurately, efficiently, and confidently classifying hands and fingers through the interpretability of prominent features. This research seeks to improve a compact, robust, explainable, and non-linear feature extraction-based CNN model for robust fingerprint pattern analysis and accurate yet efficient fingerprint classification. The proposed model (a) recognizes gender, hands, and fingers correctly through an advanced channel-wise attention-based feature extraction procedure, (b) accelerates the fingerprints identification process by applying an innovative fractional optimizer within a simple, but effective classification architecture, and (c) interprets prominent features through an explainable artificial intelligence technique. The encapsulated dependencies among distinct complex features are captured through a non-linear activation operation within a customized CNN model. The proposed fractionally optimized convolutional neural network (FOCNN) model demonstrates improved performance compared to some existing models, achieving high accuracies of 97.85%, 99.10%, and 99.29% for finger, gender, and hand classification, respectively, utilizing the benchmark Sokoto Coventry Fingerprint Dataset.

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