AI Research Intern – Deep Learning Emotion Recognition Pipeline
OtherImageEmotion Recognition
I participated in the design and optimization of deep learning models for emotion recognition in images and video for autonomous systems. The work involved training and evaluating models using custom datasets and benchmarking their real-time performance. I utilized PyTorch, OpenCV, MediaPipe, and DeepFace to develop pipelines that identify and label facial emotions. • Designed and built a custom ResNet-CBAM model for emotion classification
• Integrated MediaPipe Face Mesh for key point extraction and multi-face tracking
• Applied DeepFace for precise emotional state prediction
• Achieved 85% real-time emotion recognition system accuracy in testing environments.
I participated in the design and optimization of deep learning models for emotion recognition in images and video for autonomous systems. The work involved training and evaluating models using custom datasets and benchmarking their real-time performance. I utilized PyTorch, OpenCV, MediaPipe, and DeepFace to develop pipelines that identify and label facial emotions. • Designed and built a custom ResNet-CBAM model for emotion classification • Integrated MediaPipe Face Mesh for key point extraction and multi-face tracking • Applied DeepFace for precise emotional state prediction • Achieved 85% real-time emotion recognition system accuracy in testing environments.
2025 - 2025