@Article{cmes.2025.068131,
AUTHOR = {Mubeen Sabir, Zeshan Aslam Khan, Muhammad Waqar, Khizer Mehmood, Muhammad Junaid Ali Asif Raja, Naveed Ishtiaq Chaudhary, Khalid Mehmood Cheema, Muhammad Asif Zahoor Raja, Muhammad Farhan Khan, Syed Sohail Ahmed},
TITLE = {Systematic Analysis of Latent Fingerprint Patterns through Fractionally Optimized CNN Model for Interpretable Multi-Output Identification},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {145},
YEAR = {2025},
NUMBER = {1},
PAGES = {807--855},
URL = {http://www.techscience.com/CMES/v145n1/64323},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2025.068131}
}