sid230798/Facial-emotion-Recognition
This Repo consist code for transfer learning for facial emotion detection via valence and arousal levels. We used pretrained weights from VGG-16 net and apply on that features deep neural network and lstm model in pytorch. We tested our model on Aff-wild net dataset.
This tool helps researchers and analysts automatically categorize human facial expressions from video footage. By processing video frames, it identifies and quantifies the underlying emotional state, expressed as valence (how positive or negative) and arousal (how intense). This is useful for anyone studying emotional responses in naturalistic settings.
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Use this if you need to analyze emotional content from video data to understand human sentiment or reactions.
Not ideal if you need real-time emotion detection on a live feed or require highly specialized emotion categories beyond valence and arousal.
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May 03, 2020
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