Facial Image Completion Using Bi-Directional Pixel LSTM

Abstract

Structural features of facial images directly affect the performance of the image completion model. However, most existing work does not make full use of spatial dependence to extract features, and cause the semantics and structure of completion being inconsistent with the context. This paper addresses this issue using a bi-directional pixel long-short time memory (LSTM) network. Specifically, it consists of two LSTM subnetworks and can simultaneously scan the input image row by row or column by column, thereby the extracted features contain the dependence information among rows or among columns. Through a fusion operation of these features, the complete spatial dependence information is included. In addition, the parameters of the decoder and discriminator are automatically adjusted to accommodate the proposed bi-directional pixel LSTM. Finally, compared with existing state-of-the-art methods, experimental results show our superior performance.

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“…Bidirectional LSTMs [29] are a kind of LSTM that can be used to increase model performance on sequence classification issues. Bidirectional long-short term memory is the process of allowing any neural network to store sequence information in both backward (future to past) and forward (forward to future) directions.…”