Abstract
Automatic human gender identification can help in a developing number of applications related to human–computer interaction (HCI), human–robot interaction and surveillances technologies. Besides, it can also assist in human face identification by reducing the issue of comparing to half of the database. Several biometrics have been used to identify the human gender, but no significant achievements have been reported in the literature. In this study, we have taken palmprint biometrics, because it contains sufficient significant discriminating information like ridges, wrinkles, and principal lines. Based on it, we are going to propose an algorithm for automatic human gender identification. It involves three steps: extraction of ROI, features computation, and classification. Gabor wavelets are employed to extract the palmprint features as they are potential in capturing discriminating textural properties of the underlying image. Its performance is evaluated with simple KNN classifier on publicly available CASIA palmprint Database. The results obtained are quite encouraging with average accuracy of 97.90% with 10 cross validation.
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Acknowledgements
The Portions of the research in this paper uses the CASIA Palmprint Database collected by the Chinese Academy of Science Institute of Automation (CASIA). Authors thanks to Chinese Academy of Science Institute of Automation for providing the database for conducting the experiment.
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Gornale, S.S., Patil, A., Hangarge, M., Pardesi, R. (2019). Automatic Human Gender Identification Using Palmprint. In: Luhach, A.K., Hawari, K.B.G., Mihai, I.C., Hsiung, PA., Mishra, R.B. (eds) Smart Computational Strategies: Theoretical and Practical Aspects. Springer, Singapore. https://doi.org/10.1007/978-981-13-6295-8_5
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