pSahoo-456/Enhanced-Facial-Emotion-Recognition-Using-Transfer-Learning-with-ResNet152
Emotion classification model leveraging Transfer Learning with ResNet152 for robust facial emotion recognition across six categories, featuring preprocessing, augmentation, and detailed evaluation metrics.
This project helps identify six human emotions (Happy, Angry, Sad, Neutral, Surprise, and Ahegao) from facial images or live camera feeds. You provide an image of a face, and it tells you the most likely emotion expressed. This is useful for professionals in fields like human-computer interaction, mental health assessment, or intelligent camera system development.
Use this if you need to automatically detect and classify emotions from facial expressions with high accuracy, even in varied real-world conditions.
Not ideal if you need to detect a broader range of emotions beyond the six specified, or if you require very low computational overhead for extremely resource-constrained devices.
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MIT
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Last pushed
Nov 26, 2025
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