2023/10/14 by Ashley Jisue Hong, Hong, Ashley Jisue, David DiStefano +2
Medicine · Psychology · #COVID-19 diagnosis using AI #Emotion and Mood Recognition #FOS: Computer and information sciences #I.2.6 #I.4.m #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.2310.09473
openalex publication_date 2023/10/14 · openalex created_date 2023/10/18 · openalex updated_date 2026/07/28
Emotional Artificial Intelligences are currently one of the most anticipated developments of AI. If successful, these AIs will be classified as one of the most complex, intelligent nonhuman entities as they will possess sentience, the primary factor that distinguishes living humans and mechanical machines. For AIs to be classified as "emotional," they should be able to empathize with others and classify their emotions because without such abilities they cannot normally interact with humans. This study investigates the CNN model's ability to recognize and classify human facial expressions (positive, neutral, negative). The CNN model made for this study is programmed in Python and trained with preprocessed data from the Chicago Face Database. The model is intentionally designed with less complexity to further investigate its ability. We hypothesized that the model will perform better than chance (33.3%) in classifying each emotion class of input data. The model accuracy was tested with novel images. Accuracy was summarized in a percentage report, comparative plot, and confusion matrix. Results of this study supported the hypothesis as the model had 75% accuracy over 10,000 images (data), highlighting the possibility of AIs that accurately analyze human emotions and the prospect of viable Emotional AIs.