An Advanced Facial Expression Detection using Deep Neural Network

Arnold Sachith A Hans, Mohit Bansal, S. Rao
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Abstract

Face serves as the primary source of contact for humans while interacting with each other. Facial Expressions fall under the bucket of non-verbal type of communication and plays a vital role in understanding the emotional state of a person. Identifying emotions through Facial Expressions can be used in various fields like revealing the Behavior of a candidate in a Job Interview, Understanding the comprehension level of the candidates in a classroom, Healthcare, Electoral campaign etc.; In additions to images fed to the Neural Network, Open Face tool is also used to extract the Facial Action Units of the subject in the dataset which contributes in training a neural network. We have designed and built a model-based technique with a high accuracy to classify the Facial based emotions. The data is trained on a Temporal Relations based Neural Network. Emotions can help us make decisions and it has a wide use case.
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基于深度神经网络的高级面部表情检测
脸是人类互动的主要接触来源。面部表情属于非语言类型的交流,在理解一个人的情绪状态方面起着至关重要的作用。通过面部表情识别情绪可以应用于许多领域,如在面试中揭示候选人的行为,在课堂上了解候选人的理解水平,医疗保健,选举活动等;除了提供给神经网络的图像外,Open Face工具还用于提取数据集中受试者的面部动作单元,这有助于训练神经网络。我们设计并构建了一种基于模型的技术,对基于面部的情绪进行了高精度的分类。数据在基于时间关系的神经网络上进行训练。情绪可以帮助我们做出决定,它有广泛的用例。
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