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The automatic recognition of a person's emotional state has become a veryactive research field that involves scientists specialized in different areassuch as artificial intelligence, computer vision or psychology, among others.Our main objective in this work is to develop a novel approach, usingpersistent entropy and neural networks as main tools, to recognise and classifyemotions from talking-face videos. Specifically, we combine audio-signal andimage-sequence information to compute a topology signature(a 9-dimensionalvector) for each video. We prove that small changes in the video produce smallchanges in the signature. These topological signatures are used to feed aneural network to distinguish between the following emotions: neutral, calm,happy, sad, angry, fearful, disgust, and surprised. The results reached arepromising and competitive, beating the performance reached in otherstate-of-the-art works found in the literature.

Emotion recognition in talking-face videos using persistent entropy and neural networks