Gender Classification from Fingerprint Using Hybrid CNN-SVM

Abstract

Gender classification is used in numerous applications such as biometrics, criminology, surveillance, HCI, and business profiling. Although biometric factors like gait, face, hand shape, and iris have been used to classify people into genders, the majority of research has focused on facial traits due to their more recognisable qualities. This research employs fingerprints to classify gender, with the intention of being relevant for future studies. Several methods for gender classification utilising fingerprints have been presented in the literature, including ANN, KNN, Naive Bayes, the Gaussian mixture model, and deep learning-based classifiers. Although these classifiers have shown good classification accuracy, gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy, computation, and running time. In this paper, a CNN-SVM hybrid framework for gender classification from fingerprints is proposed, where preprocessing, feature extraction, and classification are the three main components. The main goal of this study is to use CNN to extract fingerprint information. These features are then sent to an SVM classifier to determine gender. The hybrid model's performance measures are examined and compared to those of the conventional CNN model. Using a CNN-SVM hybrid model, the accuracy of gender classification based on fingerprints was 99.25%.