Face Expression Recognition With Convolutional Neural Networks (cnn)

Résumé: Face expression recognition plays a vital role in various fields, including human- computer interaction, affective computing, and social robotics. Traditional methods for face expression recognition heavily rely on handcrafted features and shallow classifiers, limiting their ability to capture intricate facial dynamics. In recent years, deep learning techniques have emerged as a powerful tool for tackling complex vision tasks, offering promising advancements in face expression recognition. This thesis focuses on the development and evaluation of a deep learning-based approach for face expression recognition. The primary objective is to leverage the expressive capacity of deep neural networks to accurately detect and classify facial expressions from static images or video sequences. The proposed methodology involves several key stages, including data pre-processing, feature extraction, network architecture design, model training, and performance evaluation. To facilitate this research, a comprehensive dataset of labeled facial expressions is collected, comprising diverse individuals from different demographic groups. The dataset is carefully curated and annotated to ensure robustness and generalizability of the trained models. Preprocessing techniques such as face alignment, image augmentation, and illumination normalization are applied to enhance the quality and consistency of the data. For feature extraction, various deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are investigated to capture both spatial and temporal dependencies in facial expressions. The chosen architecture is optimized to balance model complexity and performance. Transfer learning techniques are explored to leverage pre-trained models and accelerate the training process. Extensive experiments are conducted to evaluate the proposed methodology on the collected dataset and compare its performance against existing state-of-the-art methods. Various evaluation metrics, including accuracy, precision, recall, and F1-score, are employed to VI assess the robustness and generalization capabilities of the developed models. The results demonstrate the effectiveness and superiority of the deep learning-based approach in face expression recognition. Furthermore, the thesis explores the impact of different factors, such as pose variations, occlusions, and individual differences, on the performance of the proposed models. Mitigation strategies and adaptation techniques are investigated to enhance the models' robustness and adaptability in real-world scenarios. The outcomes of this research contribute to the advancement of face expression recognition techniques, providing valuable insights into the potential applications of deep learning in this domain. The proposed methodology showcases the ability of deep neural networks to accurately detect and classify facial expressions, thus opening avenues for further research and practical implementations in fields like human-computer interaction, affective

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