Partial Face Detection and Illumination Estimation
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Face Analysis has long been a crucial component of many security applications. In this work, we shall propose and explore some face analysis algorithms which are applicable to two different security problems, namely Active Authentication and Image Tampering Detection. In the first section, we propose two algorithms, “Deep Feature based Face Detection for Mobile Devices” and “DeepSegFace” that are useful in detecting partial faces such as those seem in typical Active Authentication scenarios. In the second section, we propose an algorithm to detect discrepancies in illumination conditions given two face images, and use that as an indication to decide if an image has been tampered by transplanting faces. We also extend the illumination detection algorithm by proposing an adversarial data augmentation scheme. We show the efficacy of the proposed algorithms by evaluating them on multiple datasets.