Browsing by Author "Heredia Parillo, Juanpablo Andrew"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A multi-modal emotion recogniser based on the integration of multiple fusion methods
    (Universidad Católica San Pablo, 2021) Heredia Parillo, Juanpablo Andrew; Ticona Herrera, Regina Paola
    People naturally express emotions in simultaneous different ways. Thus, multimodal methods are becoming popular for emotion recognition and analysis of reactions to many aspects of daily life. This research work presents a multimodal method for emotion recognition from images. The multi-modal method analyses facial expressions, body gestures and the characteristics of the body and the environment to determine an emotional state, processing each modality with a specialised deep learning model and then applying the proposed fusion method. The fusion method, called EmbraceNet+, consists of a branched architecture that integrates the EmbraceNet fusion method with other fusion methods. The tests carried out on an adaptation of the EMOTIC dataset show that the proposed multi-modal method is effective and improves the results obtained by individual processings, as well as competing with other state-ofthe-art methods. The proposed method has many areas of application because it seeks to recognise emotions in any situation. Likewise, the proposed fusion method can be used in any multi-modal deep learning-based model.
  • Thumbnail Image
    Item
    An automatic emotion recognition system that uses the human body posture
    (Universidad Católica San Pablo, 2021) Heredia Parillo, Juanpablo Andrew; Ticona Herrera, Regina Paola
    Non-verbal communication is very present in our lives, but it can be interpreted in different ways according to many factors. With nonverbal gestures people can express explicit and implicit messages, which makes them important to understand. Computer vision methods for recognising body gestures and machine learning classification methods offer an opportunity to understand what people express with their bodies. This research work focuses on the emotions expressed by body gestures, particularly the posture. Thus, an automatic emotion recognition system from images is proposed, which uses a graph convolutional neural network to perform the classification. Generally, deep learning approach needs many training samples, but these are difficult to obtain for posture emotion recognition, thus, the proposed model trains under a meta-learning algorithm based on the “agnostic model”, which allows training with few examples. Only the meta-learning algorithm was tested, which demonstrated the adaptability and expands the applicability of the graph convolutional neural networks.