Robust LSTM-Autoencoders for Face De-Occlusion in the Wild

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Fang Zhao Shuicheng Yan

Abstract

Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still challenging for existing face recognizers, which is heavily desired in real-world applications concerning surveillance and security. Although much research effort has been devoted to developing face de-occlusion methods, most of them can only work well under constrained conditions, such as all of faces are from a pre-defined closed set of subjects. In this paper, we propose a robust LSTM-Autoencoders (RLA) model to effectively restore partially occluded faces even in the wild. The RLA model consists of two LSTM components, which aims at occlusion-robust face encoding and recurrent occlusion removal respectively. The first one, named multi-scale spatial LSTM encoder, reads facial patches of various scales sequentially to output a latent representation, and occlusion-robustness is achieved owing to the fact that the influence of occlusion is only upon some of the patches. Receiving the representation learned by the encoder, the LSTM decoder with a dual channel architecture reconstructs the overall face and detects occlusion simultaneously, and by feat of LSTM, the decoder breaks down the task o...Continue Reading

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Citations

Apr 23, 2019·I-Perception·Jordy ThielenRob van Lier
Aug 12, 2021·Neuroinformatics·Parikshat SirpalFrédéric Lesage

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