Abstract
Over the past few decades, emotion detection in a real-time environment has been a progressive area of research. This study aims to identify physically challenged individuals and the cognitive gestures of Autism children using Convolutional Neural Network (CNN) based on facial landmarks by creating the emotion detection algorithm. The algorithm is used in real-time by using virtual markers that perform efficiently in the uneven spotlight and head rotation, multiple backgrounds and distinct facial tones. Emotions of faces such as happiness, sorrow, rage, Surprise, Disgust, Fear using virtual markers are collected. Initially, the faces are detected using a cascade classifier. After face image detection, the image is given to the pre-processing stage to remove the noise present in the image. This process is used to increase classification accuracy. Finally, the images are given to the convolution neural network (CNN) classifier to classify the emotions such as happiness, sorrow, rage, Surprise, Disgust and Fear. The performance of the proposed approach is analyzed based on accuracy, precision, recall and F-measure. The proposed Emotion detection using CNN has achieved a cumulative recognition rate of 99.81%.
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Umamaheswari, K.M., Vignesh, M.T. (2023). Face Emotion Detection for Autism Children Using Convolutional Neural Network Algorithms. In: Biswas, A., Semwal, V.B., Singh, D. (eds) Artificial Intelligence for Societal Issues. Intelligent Systems Reference Library, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-12419-8_10
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