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In a typical face recognition pipeline, the task of the face detector is tolocalize the face region. However, the face detector localizes regions thatlook like a face, irrespective of the liveliness of the face, which makes theentire system susceptible to presentation attacks. In this work, we try toreformulate the task of the face detector to detect real faces, thuseliminating the threat of presentation attacks. While this task could bechallenging with visible spectrum images alone, we leverage the multi-channelinformation available from off the shelf devices (such as color, depth, andinfrared channels) to design a multi-channel face detector. The proposed systemcan be used as a live-face detector obviating the need for a separatepresentation attack detection module, making the system reliable in practicewithout any additional computational overhead. The main idea is to leverage asingle-stage object detection framework, with a joint representation obtainedfrom different channels for the PAD task. We have evaluated our approach in themulti-channel WMCA dataset containing a wide variety of attacks to show theeffectiveness of the proposed framework.

Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector