Facial Expression Recognition via Closed-Loop Transcription

Convolutional Neural Networks (CNNs) are the most widely used and successful approaches for accomplishing the Facial Expression Recognition (FER) task. However, these technologies appear to have reached a bottleneck since the networks are becoming increasingly complex and heavier, yet the performance improvements remain minimal. The "black box" nature of CNNs makes it challenging to identify and address this crux. This paper aims to address the issue by utilizing the principles of data compression and discriminative representation within a plausible theoretical framework. We are employing an innovative computational framework, drawing inspiration from feedback loop control systems, to develop an interpretable closed-loop transcription model to generate a Linear Discriminative Representation (LDR) for multi-class real-world datasets. This model, comprised of an encoder and decoder that facilitates data mapping between the data space and the feature space, employs the output of the transcription system as feedback or guidance, with the objective of improving the accuracy and efficiency of the transcription process. The optimal LDR is learned jointly for all data classes through a two-player minimax game between the encoder and decoder for the learned representation over a single rate reduction-based objective. The studies conducted on the FER2013 dataset show promising results for the proposed closed-loop formulation. The visual quality of the learned decoder and the classification performance of the encoder are comparable to those of large and complex networks. An accuracy of 70.97% was attained on the FER2013 dataset by applying Principal Component Analysis (PCA) to the learned representation.