| Summary: | 碩士 === 國立臺北科技大學 === 資訊工程系 === 107 === In daily life, people often express emotions through ways other than language. Among them, people’s facial expression is the most common way to express their emotion, people can take corresponding actions to achieve their goals via facial expression. Thus the emotion recognition has become an important technology in the artificial intelligence industry, the related work including Safe Driving, Commercial Advertising Evaluation, etc. Although there are several emotion recognition models for adult appeared in recent years, they didn’t have better performance on children facial expression through children has different facial features from adult’s. To solve this problem, this research takes four different classes in the kindergarten as the dataset, building children emotion recognition model based on Convolutional Neural Network. First, this research cropped and converted the dataset picture to grayscale, and labeled an emotion in seven classes as the right result for the picture, including angry, disgust, fear, happiness, sadness, surprise, neutral, contempt. Then input these data into the Convolutional Neural Network. Since Convolutional Neural Network is different from the neural network based on traditional feature engineering, it can learn the emotional features for the classes itself without manual setting, At the same time, this research added residual modules and depth-wise separable convolutions to reduce the depth and parameter in the Convolutional Neural Network. At last, this research used a support vector machine to classify the result. After finishing the training, this research used the test data to evaluate the accuracy of the model, to prove the model can effectively recognize children's expression emotions.
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