This study proposes a convolutional neural network (CNN) based framework for predicting gender from fingerprint images. Gender prediction from biometric data has become an important area of research in security, forensics, and human-computer interaction. Unlike traditional fingerprint systems that focus on identity verification, the proposed model automatically learns discriminative fingerprint features, including ridge density, minutiae distribution, and texture patterns, directly from raw images. A total of 6,000 fingerprint images from the SOCOFing dataset, representing both male and female individuals of Sub-Saharan African origin, were used for model development. The data underwent comprehensive preprocessing, including labeling, normalization, enhancement, and segmentation, before being partitioned into training (70%), validation (10%), and testing (20%) subsets. Experimental results demonstrate that the CNN model achieves an overall accuracy of 95%, with precision and recall exceeding 93% for both male and female classes. The area under the ROC curve (AUC) was 0.97 for males and 0.96 for females, indicating excellent discriminative ability. These findings highlight the effectiveness of deep learning for automated gender prediction from fingerprints and suggest its potential applications in biometric authentication, forensic analysis, security, and personalized services. The proposed framework eliminates the challenges of conventional feature engineering and provides a robust, scalable, and accurate solution for gender classification using fingerprint biometrics.
Ogunlana et al. (Tue,) studied this question.