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.
Classification of gender from fingerprints is one of the important steps in forensic anthropology. This forensic anthropology is used to identify the gender of a criminal in order to minimize the suspects list of search. A very few researcher have worked on gender classification using fingerprints and have gain the competitive results. In this work we are trying to fuse the fingerprint and age biometrics for gender classification. The real fingerprints were collected from different age groups such as 15-20 years and 20-60 years of the rural and urban people. According to this experimental observation soft biometric information can be used significantly to improve the recognition performance of biometric system. The overall performance of the proposed method is found to be satisfactory and more competitive.
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