Experimental study demonstrates reconstructed facial features restore recognition accuracy in occluded surveillance imagery, highlighting benefits of combined enhancement methods.
Abstract Face occlusions on CCTV cameras obscure important key facial features, preventing face recognition (FR) systems from recognizing people. This work mainly focuses on reconstructing these missing facial parts using Generative Adversarial Neural Networks (GANs) to improve FR accuracy while maintaining a low False Acceptance Rate (FAR). In addition, we are trying to improve the generated images further by using different image enhancement methods to test whether they can be used to improve the FR accuracy. To test the results, we perform experiments using state-of-the-art FR methods such as QMagFace and ArcFace to see whether image reconstruction and image enhancement help to improve FR accuracy.
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Pleško et al. (2024) studied this question.
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