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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Conditional convolution neural network enhanced random forest for facial expression recognition
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Conditional convolution neural network enhanced random forest for facial expression recognition

机译：条件卷积神经网络增强随机林面部表情识别

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摘要

In real-world applications, factors such as head pose variation, occlusion, and poor image quality make facial expression recognition (FER) an open challenge. In this paper, a novel conditional convolutional neural network enhanced random forest (CoNERF) is proposed for FER in unconstrained environment. Our method extracts robust deep salient features from saliency-guided facial patches to reduce the influence from various distortion types, such as illumination, occlusion, low image resolution, etc. A conditional CoNERF is devised to enhance decision trees with the capability of representation learning from transferred convolutional neural networks and to model facial expression of different perspectives with conditional probabilistic learning. In the learning process, we introduce a neurally connected split function (NCSF) as the node splitting strategy in the CoNERF. Experiments were conducted using public CK+, JAFFE, multi-view BU-3DEF and LFW datasets. Compared to the state-of-the-art methods, the proposed method achieved much improved performance and great robustness with an average accuracy of 94.09% on the multi-view BU-3DEF dataset, 99.02% on CK+ and JAFFE frontal facial datasets, and 60.9% on LFW dataset. In addition, in contrast to deep neural networks which require large-scale training data, conditional CoNERF performs well even when there are only a small amount of training data. (C) 2018 Elsevier Ltd. All rights reserved.
机译：在现实世界的应用中，头部姿势变化，闭塞等因素，图像质量差，使面部表情识别（FER）开放挑战。本文提出了一种新颖的条件卷积神经网络增强的随机森林（CONERF）在无约束环境中。我们的方法从显着引导的面部贴片中提取强大的深度凸起特征，以减少各种失真类型的影响，例如照明，遮挡，低图像分辨率等。设计有条件的锥体，以增强具有表示学习的能力的决策树转移卷积神经网络，并用条件概率学习模拟不同观点的面部表达。在学习过程中，我们将一个神经连接的拆分功能（NCSF）引入CONERF中的节点拆分策略。使用公共CK +，jaffe，多视图Bu-3def和LFW数据集进行实验。与最先进的方法相比，所提出的方法在多视图BU-3DEF数据集中实现了大量提高的性能和巨大的稳健性，平均精度为94.09％，CK +和jaffe额面面部数据集99.02％ LFW数据集60.9％。另外，与需要大规模训练数据的深度神经网络相比，即使只有少量训练数据，条件锥体也表现良好。 （c）2018年elestvier有限公司保留所有权利。

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