On the Data Conditioning for Facial Spoofing Attacks Detection using Deep Learning
Raphael Ruschel, Lucas Royes Schardosim, Jacob Scharcanski · 2019
Biometry-based authentication systems are potential candidates to replace traditional username and password-based access schemes. Facial recognition is becoming widely popular, and many existing devices already include embedded cameras, making this technology easy to use. Nevertheless, facial recognition systems are prone to security breaches, such as facial spoofing attacks, where a impostor tries to gain access to the system by disguising him/herself as a genuine user. The goal of this paper is to propose a countermeasure capable of detecting unauthorized access attempts in facial recognition systems. Most authors uses only the face to detect facial spoofing attacks. However, we argue that more information available in the training data found on Presentation Attack Detection (PAD) datasets should be used, specially when adopting deep learning schemes. We show that using the full frame captured by the camera is more reliable than using only the face since the environment presents rich information that is useful to differentiate a genuine access from an impostor. We present a deep learning method that uses the entire frame instead of just the face to detect presentation attacks. The preliminary experimental results are encouraging, and based on a GoogLeNet architecture, the detection of such attacks potentially can be obtained in more than 96% of the test cases.