Using ResNet Transfer Deep Learning Methods in Person Identification According to Physical Actions

Creative Commons License

Kilic S., Askerbeyli İ., Kaya Y.

IEEE ACCESS, cilt.8, ss.220364-220373, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 8
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1109/access.2020.3040649
  • Dergi Adı: IEEE ACCESS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.220364-220373
  • Anahtar Kelimeler: Biometrics (access control), Identification of persons, Sensors, Deep learning, Magnetic sensors, Accelerometers, Object recognition, Transfer deep learning models, ResNet, person identification, wearable sensor, biometric system, ECG, EEG, RECOGNITION
  • Ankara Üniversitesi Adresli: Evet

Özet

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.