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An ensemble convolutional echo state networks for facial expression recognition

机译：用于面部表情识别的集合卷积回声状态网络

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摘要

Facial expressions recognition (FER) plays a much important role in various applications from human-computer interfaces to psychological tests. However, most methods are confronted with the quality of the face images, vanishing gradients problem, over-trained problem, difference of face images such as in age and ethnicity, mulitple parameters required tuning, and dubious class labels in the training data. These negative factors largely hurt the recognition performance. To alleviate these problems, this paper proposes an new approach named ensemble convolutional echo state network, which takes Echo State Network (ESN) as the base classifier for ensemble and Convolutional Network (CN) to transform the input face image for further feeding to ESN, where the random parameters and architectures are assigned to ensure the diversity of the ensemble and to avoid computing stochastic gradient. Based on the rich dynamics of ESN and rich variations of input face image finished by CN, the proposed approach has the great ability to deal with the real facial expression recognition and to be scaled to the larger training data. It has also only one parameter to be adjusted. Conducted experiments show that the method achieves significant improvement over current methods on person-independent facial expression recognition.
机译：面部表情识别（FER）在来自人机界面到心理测试的各种应用中起着重要的作用。然而，大多数方法都面临着面部图像的质量，消失的梯度问题，过度训练的问题，面部图像的差异，诸如年龄和种族的差异，在训练数据中需要调整和可疑的类标签。这些负面因素在很大程度上损害了识别性能。为了缓解这些问题，本文提出了一种名为集合卷积回声状态网络的新方法，该方法采用回声状态网络（ESN）作为集合和卷积网络（CN）的基本分类器，以改造输入面部图像以进一步进入ESN，分配随机参数和架构以确保集合的多样性并避免计算随机梯度。基于ESN的丰富动力学和CN完成的输入面部图像的丰富变化，所提出的方法具有处理真实面部表情识别的能力，并将其缩放到较大的训练数据。它也只有一个参数要调整。进行的实验表明，该方法实现了对独立于人类的面部表情识别的目前方法的显着改善。

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