Lightweight face recognition-based portable attendance system with liveness detection

Face recognition systems that do not implement liveness detection are susceptible to face spoofing attacks. This vulnerability implies that an attacker could disguise themselves as another individual and the system would falsely take the attendance of that other individual. To prevent these attacks, a liveness detection step can be implemented before recognizing subjects. Face recognition -based attendance system devices are typically installed at the entrance to an event or space, so having a portable device that can be easily relocated is practical and efficient. Hence, face recognition systems should be lightweight enough to be able to run on portable devices with limited computational power. Implementing liveness detection will increase the system's processing time. Therefore, this study aims to develop a lightweight liveness detection method that can be run on a Raspberry Pi. To achieve this, several pre -trained models were evaluated and MobileNetV2 was chosen based on the results. The MobileNetV2 model was then trained using transfer learning method. The proposed attendance system achieved an average processing time below 0.6 s and 96 % accuracy for live subjects, 79 % accuracy for level A spoof attacks, 83.7 % accuracy for level B spoof attacks, and 70 % accuracy for level C spoof attacks.