Abstract
Individuals suffering from Autism Spectrum Disorder find it challenging to perceive basic human emotions, which deters their communication capabilities. Given this difficulty, we proposed a Kawaii-engineered framework for Individuals with Autism by developing a Machine Learning pipeline using multilabel classification algorithms to identify the emotions from a video. We experimented on two datasets OMGE and DIAEMO. Both datasets have videos of the duration of approximately one minute. After pre-processing, facial expressions and audio content-based features were extracted. Multilabel Classification algorithm like Instance Based Learning by Logistic Regression for Multi-Label Learning, Multi Label k- Nearest Neighbour, Binary Relevance k- Nearest Neighbour, Random k- Label Sets, and Calibrated Label Ranking were used and for sampling, 5-fold Cross-Validation and Leave One Out Cross Validation were employed for both the datasets. We observed that LOOCV based sampling strategy gave the best results for both the datasets with CLR classifier using Gaussian kernel.
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Seniaray, S., Gupta, T., Payal, Singh, R. (2022). Emotion Recognition for Individuals with Autism. In: Kurosu, M. (eds) Human-Computer Interaction. Technological Innovation. HCII 2022. Lecture Notes in Computer Science, vol 13303. Springer, Cham. https://doi.org/10.1007/978-3-031-05409-9_31
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