In this cutting-edge technological milieu, fingerprints have become an alternative expression for the biometrics system. A fingerprint is one of the perceptible biometric modals which is predominantly utilized in almost all the security, and real-life applications. Fingerprints have many inherent rotational features that are mostly utilized for person recognition besides these features can also be utilized for the person gender classification. Thus, the proposed work is a novel algorithm which identifies the gender of an individual based on the fingerprint. The image fusion and feature level fusion technique are deliberated over the fingerprints with rotational invariant features. Experiments were carried on four state-of-the-art datasets and realized promising results by outperforming earlier outcomes.

A certain number of researchers have utilized uni-modal bio-metric traits for gender classification. It has many limitations which can be mitigated with inclusion of multiple sources of biometric information to identify or classify user’s information. Intuitively multimodal systems are more reliable and viable solution as multiple independent characteristics of modalities are fused together. The objective of this work is inferring the gender by combining different biometric traits like face, iris, and fingerprints of same subject. In the proposed work, feature level fusion is considered to obtain robustness in gender determination; and an accuracy of 99.8% was achieved on homologous multimodal biometric database SDUMLA-HMT (Group of Machine Learning and Applications, Shandong University). The results demonstrate that the feature level fusion of Multimodal Biometric system greatly improves the performance of gender classification and our approach outperforms the state-of-the-art techniques noticed in the literature.