Deep facial expression recognition faces two challenges that both stem from the large number of trainable parameters: long training times and a lack of interpretability. We propose a novel method based on evolutionary algorithms, that deals with both challenges by massively reducing the number of trainable parameters, whilst simultaneously retaining classification performance, and in some cases achieving superior performance. We are robustly able to reduce the number of parameters on average by 95% (e.g. from 2M to 100k parameters) with no loss in classification accuracy. The algorithm learns to choose small patches from the image, relative to the nose, which carry the most important information about emotion, and which coincide with typical human choices of important features. Our work implements a novel form attention and shows that evolutionary algorithms are a valuable addition to machine learning in the deep learning era, both for reducing the number of parameters for facial expression recognition and for providing interpretable features that can help reduce bias.

中文翻译：

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面部情绪识别的深度进化

深度面部表情识别面临两个挑战，它们都源于大量可训练的参数：训练时间长和缺乏可解释性。我们提出了一种基于进化算法的新方法，该方法通过大量减少可训练参数的数量来应对这两个挑战，同时保持分类性能，并在某些情况下实现卓越的性能。我们能够稳健地将参数数量平均减少 95%（例如从 2M 到 100k 参数），而不会降低分类精度。该算法学习从图像中选择与鼻子相关的小块，这些小块携带最重要的情感信息，并且与典型的人类对重要特征的选择相吻合。