论文标题
面部情绪识别的深刻进化
Deep Evolution for Facial Emotion Recognition
论文作者
论文摘要
深层面部表情识别面临两个挑战，这两者都源于大量可训练的参数：长期训练时间和缺乏可解释性。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，同时保留分类性能，在某些情况下可以实现出色的性能来应对这两种挑战。我们非常有能力将参数的数量平均减少95％（例如，从2M到100K参数），而分类精度没有损失。该算法学会从图像中选择小斑块，相对于鼻子，其中包含有关情感的最重要信息，并且与典型的人类选择重要特征相吻合。我们的工作引起了一种新颖的关注，并表明进化算法是深度学习时代机器学习的宝贵补充，既可以减少面部表情识别的参数数量，又用于提供可解释的特征以帮助减少偏见。
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.
