Abstract: Biometric systems, particularly fingerprint recognition, are crucial for modern security and identity management. While traditionally used for authentication, recent research suggests that fingerprint characteristics can exhibit gender-specific differences. This paper explores the potential of machine learning techniques to classify an individual's gender based solely on fingerprint images. The approach involves systematically analyzing morphological features such as ridge density, ridge thickness, total ridge count, minutiae distribution, and overall texture patterns. This research aims to contribute to the expanding applications of fingerprint biometrics beyond traditional identification.

Keywords: Fingerprint Recognition, Gender Classification, Machine Learning, Biometrics, Feature Extraction, Pattern Recognition.

| DOI: 10.17148/IARJSET.2025.125173