GENDER ESTIMATION WITH CONVOLUTIONAL NEURAL NETWORKS USING FINGERTIP IMAGES
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Date
2020
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info:eu-repo/semantics/openAccess
Abstract
Bringing several innovations to our daily life, the importance of artificial intelligence technology hasbeen increasing day by day and has created new fields for researchers. Gender classification is also animportant research topic in the field of artificial intelligence. Studies on gender prediction from face,body, and even fingerprint images have been done. Also, today, biometric recognition systems havereached levels that can determine people's fingerprints, face, iris, palm prints, signature, DNA, andretina. In this study, various models were trained and tested on gender classification from fingertipimages. In the, a ready dataset was not used and finger images were collected from more than 200people. Rotation, cutting, and background reduction are applied to the collected images and madeready for the training. 4 different network models were set in the fieldwork. Data augmentation andtransfer learning were used in these models. Working in a limited area, the model we created hasachieved high-performance results, for all that the quality and angles of each image are different. Themodel proposed in this study has a performance rate of 86.39%.
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Journal of scientific reports-A (Online)
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0
Issue
45
