Classification of Eye Images by Personal Details With Transfer Learning Algorithms

Abstract

Machine learning methods are used for purposes such as learning and estimating a feature or parameter sought from a dataset by training the dataset to solve a particular problem. The transfer learning approach, aimed at transferring the ability of people to continue learning from their past knowledge and experiences to computer systems, is the transfer of the learning obtained in the solution of a particular problem so that it can be used in solving a new problem. Transferring the learning obtained in transfer learning provides some advantages over traditional machine learning methods, and these advantages are effective in the preference of transfer learning. In this study, a total of 1980 eye contour images of 96 different people were collected in order to solve the problem of recognizing people from their eye images. These collected data were classified in terms of person, age and gender. In the classification made for eye recognition, feature extraction was performed with 32 different transfer learning algorithms in the Python program and classified using the RandomForest algorithm for person estimation. According to the results of the research, 30 different classification algorithms were used, with the ResNet50 algorithm being the most successful, and the data were also classified in terms of age and gender. Thus, the highest success rates of 83.52%, 96.41% and 77.56% were obtained in person, age and gender classification, respectively. The study shows that people can be identified only by eye images obtained from a smartphone without using any special equipment, and even the characteristics of people such as age and gender can be determined. In addition, it has been concluded that eye images can be used in a more efficient and practical biometric recognition system than iris recognition.

Keywords

• eye image
• eye recognition
• transfer learning
• classification