The DenseNet based algorithm may be used to identify real-time facial expressions. Our suggested framework has an AUC of 91.2 percent on the FER-2013 dataset.
We propose a DenseNet-121 image feature extraction method, combined with convolutional neural network (CNN) for facial expression recognition.
Dec 22, 2021 · Therefore, we propose a DenseNet-121 image feature extraction method, combined with convolutional neural network (CNN) for facial expression ...
Abstract— FER is a field of study that focuses on categorizing human emotions based on their facial expressions. It may be utilized in video games, ...
Aug 29, 2021 · In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other ...
We propose a DenseNet-121 image feature extraction method, combined with convolutional neural network (CNN) for facial expression recognition.
EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild · Face Expression Detection on Kinect Using ...
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May 3, 2024 · In this work, we achieve the highest single-network classification accuracy on the FER2013 dataset. We adopt the VGGNet architecture, rigorously ...
Dec 18, 2022 · Human Facial Emotion Recognition (FER) is the technology to predict listener's emotion of static images and videos to uncover data on one's ...
Abstract: Facial Expression Recognition (FER) is a fundamental component of human communication with numerous potential applications. Convolutional neural.