Abstract
Today, biometric technologies are one of the areas of information security which are increasingly used in all areas required by human security. The subjects such as person identification (PI), age prediction, and gender recognition are among the topics of human-computer interactivity that have been commonly researched in both academic and other areas in recent years. PI is the process of identifying the person according to biometric features obtained. In this study, the PI process was carried out with ResNet transfer deep learning methods by using the signals from an accelerometer, magnetometer and gyroscope sensors attached to 5 different regions of the persons. Here, the persons were identified depending on different physical actions and effective actions in the PI were determined. Furthermore, the effective body areas have also been identified in PI. Generally, high success rates have been observed through ResNet architecture. This study has shown that the signals of wearable accelerometer, gyroscope, magnetometer sensors can be used as a new biometric system to prevent identity fraud attacks. In summary, the proposed method can be greatly beneficial for the effective use of wearable sensor signals in biometric applications.
Highlights
The problem of person identification has been one of the popular areas that the researchers put emphasis on using various methods over the last decades
The dataset consists of 19x8x60=9120 signal matrices. After these signal matrices were converted into images, the Resnet Networks (ResNets) deep transfer learning techniques were used
9120 images were extracted to test the success of our system. 5 different ResNet architectures were used
Summary
The problem of person identification has been one of the popular areas that the researchers put emphasis on using various methods over the last decades. The biometric identification systems based on physiological properties such as the face, fingerprint, hand geometry, iris and retina, or behavioral characteristics such as gait, signature and speech have been developed These methods have a major disadvantage since they can be imitated [2;3;4]. Goshvarpour and Goshvarpour developed a biometric system by using the MP (matching pursuit) coefficients of ECG signals with different machine learning methods such as PNN, Knn, and LDA They reported the success rate of the system as 99.68% [12]. In the study of San-Segundo et al [16], a biometric system was fabricated based on the signals that they have drown from their smartphones' accelerometer sensors They implemented the person identification process by applying the Gaussian mixture models to the signals obtained by making people walk.
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