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How to Avoid Depth Reconstruction in 3D Vision Tasks: Do We Need Depth in State-Of-The-Art Face Authentication?
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Amir Livne, M.Sc. Thesis Seminar
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Tuesday, 18.1.2022, 13:30
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Zoom Lecture: 94951674375
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Advisor:  Prof. R. Kimmel & Prof. A. Bronstein
Face recognition systems are frequently used in a variety of security applications in our daily lives. Some methods are designed to utilize geometric information extracted from depth sensors to overcome single-image-based recognition technologies’ weaknesses, such as vulnerability to illumination variations, large head poses, and spoofing attacks. However, the accurate acquisition of the depth profile or surface is an expensive and challenging process. We introduce a novel method to recognize faces from stereo camera systems without explicitly computing the facial surface or depth map. Instead, the raw face stereo images along with the location in the image from which the face is extracted allow a convolutional neural network model (CNN) to improve the recognition task while avoiding the need to handle the geometric structure of the face explicitly. This way, we keep the simplicity and cost-efficiency of identity authentication from a single image while enjoying the benefits of geometric data without explicitly reconstructing it.
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