Anjith George Sebastien Marcel

Abstract

In a typical face recognition pipeline, the task of the face detector is to localize the face region. However, the face detector localizes regions that look like a face, irrespective of the liveliness of the face, which makes the entire system susceptible to presentation attacks. In this work, we try to reformulate the task of the face detector to detect real faces, thus eliminating the threat of presentation attacks. While this task could be challenging with visible spectrum images alone, we leverage the multi-channel information available from off the shelf devices (such as color, depth, and infrared channels) to design a multi-channel face detector. The proposed system can be used as a live-face detector obviating the need for a separate presentation attack detection module, making the system reliable in practice without any additional computational overhead. The main idea is to leverage a single-stage object detection framework, with a joint representation obtained from different channels for the PAD task. We have evaluated our approach in the multi-channel WMCA dataset containing a wide variety of attacks to show the effectiveness of the proposed framework.