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首页> 外文期刊>Soft computing: A fusion of foundations, methodologies and applications >Weakly supervised facial expression recognition via transferred DAL-CNN and active incremental learning
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Weakly supervised facial expression recognition via transferred DAL-CNN and active incremental learning

机译：通过转移DAL-CNN和主动增量学习弱监督的面部表情识别

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摘要

In recent years, facial expression recognition (FER) has becoming a growing topic in computer vision with promising applications on virtual reality and human–robot interaction. Due to the influence of illumination, individual differences, attitude variation, etc., facial expression recognition with robust accuracy in complex environment is still an unsolved problem. Meanwhile, with the wide use of social communication, massive data are uploaded to the Internet; the effective utilization of those data is still a challenge due to noisy label phenomenon in the study of FER. To resolve the above-mentioned problems, firstly, a double active layer-based CNN is established to recognize the facial expression with high accuracy by learning robust and discriminative features from the data, which could enhance the robustness of network. Secondly, an active incremental learning method was utilized to tackle the problem of using Internet data. During the training phase, a two-stage transfer learning method is explored to transfer the relative information from face recognition to FER task to alleviate the inadequate training data in deep convolution network. Besides, in order to make better use of facial expression data from Web site and further improve the FER accuracy, Unconstrained Facial Expression Database from Web site database is built in this paper. Extensive experiments performed on two public facial expression recognition databases FER 2013 and SFEW 2.0 have demonstrated that the proposed scheme outperforms the state-of-the-art methods, which could achieve 67.08% and 51.90%, respectively.
机译：近年来，面部表情识别（FER）在计算机愿景中成为一种日益增长的话题，具有关于虚拟现实和人机互动的有前途的应用。由于照明的影响，个人差异，姿态变化等，在复杂环境中具有鲁棒精度的面部表情识别仍然是一个未解决的问题。同时，随着社交沟通的广泛使用，大规模数据上传到互联网;由于在FER的研究中，由于嘈杂的标签现象，这些数据的有效利用仍然是一项挑战。为了解决上述问题，首先，建立双有源层的CNN，以通过学习来自数据的稳健和识别特征来识别面部表情，这可以提高网络的鲁棒性。其次，利用有效增量学习方法来解决使用互联网数据的问题。在培训阶段，探索了两阶段转移学习方法，以将相对信息从人脸识别转移到FER任务，以减轻深度卷积网络中的培训数据不足。此外，为了更好地利用来自网站的面部表情数据并进一步提高FER精度，本文建立了来自Web站点数据库的无限制的面部表情数据库。在两个公共面部表情识别数据库FER 2013和SFEW 2.0上进行的广泛实验表明，该方案优于最先进的方法，其分别达到67.08％和51.90％。

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