2025-26 (Autonomous)

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Browsing 2025-26 (Autonomous) by Author "Shashank TJ 1NH23CE403 Niskarsh Naulakha 1NH22CE031 Naveen Kumar 1NH23CE402 Sanjay A 1NH22CE046"

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    Emotion Recognition with deep learning
    (New Horizon College of Engineering, 2026) Shashank TJ 1NH23CE403 Niskarsh Naulakha 1NH22CE031 Naveen Kumar 1NH23CE402 Sanjay A 1NH22CE046
    Emotion recognition using deep learning has emerged as a pivotal area of research, leveraging advanced neural network architectures to analyse and interpret human emotions from various modalities, including speech, text, and facial expressions. This mini project explores the integration of deep learning techniques in emotion recognition systems, focusing on their effectiveness, challenges, and applications. The project begins with a comprehensive literature review, highlighting the evolution of emotion recognition technologies and the role of deep learning in enhancing accuracy and efficiency. Traditional methods often relied on handcrafted features and shallow learning algorithms, which limited their performance. In contrast, deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have demonstrated superior capabilities in automatically extracting relevant features from raw data, thus improving recognition rates. A significant portion of the project is dedicated to the implementation of a deep learning model for emotion recognition. The chosen model is trained on a diverse dataset comprising audio, visual, and textual inputs, allowing for a multimodal approach. Data preprocessing techniques, such as normalization and augmentation, are employed to enhance the robustness of the model. The training process involves optimizing hyperparameters and employing techniques like dropout and batch normalization to prevent overfitting. The evaluation of the model's performance is conducted using various metrics, including accuracy, precision, recall, and F1-score. The results indicate that the deep learning model outperforms traditional methods, achieving high accuracy in recognizing emotions such as happiness, sadness, anger, and surprise. Additionally, the project discusses the importance of dataset diversity and quality in training effective emotion recognition systems.