GENDER ESTIMATION WITH CONVOLUTIONAL NEURAL NETWORKS USING FINGERTIP IMAGES

Abstract

Bringing several innovations to our daily life, the importance of artificial intelligence technology has been increasing day by day and has created new fields for researchers. Gender classification is also an important 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 have reached levels that can determine people's fingerprints, face, iris, palm prints, signature, DNA, and retina. In this study, various models were trained and tested on gender classification from fingertip images. In the, a ready dataset was not used and finger images were collected from more than 200 people. Rotation, cutting, and background reduction are applied to the collected images and made ready for the training. 4 different network models were set in the fieldwork. Data augmentation and transfer learning were used in these models. Working in a limited area, the model we created has achieved high-performance results, for all that the quality and angles of each image are different. The model proposed in this study has a performance rate of 86.39%.

Keywords

• Gender Prediction
• Deep Learning
• Fingertip

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