{"repo":"CoreyLeath-code/Facial-Emotion-Recognition-System","free":true,"listed":false,"github":"https://github.com/CoreyLeath-code/Facial-Emotion-Recognition-System","clone":"git clone https://github.com/CoreyLeath-code/Facial-Emotion-Recognition-System.git","description":"The **Facial Emotion Recognition System** is a robust computer vision pipeline that detects and classifies human emotions (e.g., happy, sad, angry, surprised) from facial images and video streams. It leverages transfer learning with state-of-the-art convolutional neural networks (e.g., ResNet, EfficientNet) in PyTorch, fine-tuned on the FER2013 ben","language":"Python","stars":23,"topics":["augmentation","ci-cd","computer-vision","containerization","data-preparation","deeplearning","deployment","documentation","governance","inference-api"],"license":"MIT","category":"deployment-docker-iac","readme_excerpt":"Facial Expression Classification - Reproducible Research Scaffold Abstract This repository contains a PyTorch seven-class facial-expression classifier for FER-style 48x48 grayscale images, a FastAPI inference boundary, and research-oriented reference implementations for stable softmax, cross entropy, confusion matrices, and macro metrics. No reviewed checkpoint and immutable held-out dataset artifact are committed. Therefore, accuracy, macro F1, calibration, latency, and throughput are intentionally reported as TBD. This is a reproducible research scaffold and engineering demonstration, not a validated emotion-measurement system. Facial expressions do not reliably reveal a person's internal emotional state. Do not use this project for medical, employment, education, policing, surveillance, access-control, or other consequential decisions. Research questions 1. Does the PyTorch CNN improve on a majority-class baseline on the immutable FER2013 PrivateTest split? 2. How do normalization, CNN capacity, and seed affect macro F1 and per-class recall? 3. Which classes produce stable confusion patterns across repeated runs? 4. What accuracy-latency tradeoff appears under fixed weights, hardware, and batch settings? Formal problem statement The supported model maps an image x to seven logits z = f theta(x). Inference turns them into class probabilities with stable softmax and selects the largest probability. Cross-entropy is the training loss represented by the legacy TensorFlow scrip","default_branch":null,"files":null,"tree":[],"storefront":"/r/CoreyLeath-code","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/CoreyLeath-code/Facial-Emotion-Recognition-System/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}