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You will get Advanced Emotion Detection Software with Seamless NLP Integration
Project details
A Facial Expression Detection system that uses a CNN trained on top of the Yolo model using a facial expression dataset. This model accurately recognizes and classifies human emotions from both real-time webcam input and uploaded images. The system supports several emotion categories including Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral, etc., making it suitable for a wide range of applications in emotion analysis such as user experience determination and human-computer interaction.
Programming Languages
Python, C#What's included $200
These options are included with the project scope.
$200
- Delivery Time 1 day
- Number of Revisions 2
- Design Customization
- Content Upload
- Responsive Design
- Source Code
About Pradeeba
Data Science| AI&ML| Deep Learning| Computer Vision| LLMs
Chennai, India - 12:15 pm local time
I’m proficient in object classification using YOLO models. I have hands-on experience with all major YOLO versions, including YOLOv1, YOLOv5, YOLOv7, YOLOv8 and YOLO11.
I’ve worked with PoseNet, and MoveNet for action recognition.
My computer vision work involves OpenCV, SLAM, TensorFlow, PyTorch, ONNX, CUDA, TensorRT, OpenVINO, and NPUs. I use Albumentations for image augmentation, DeepSORT and SORT for object tracking, and perform image preprocessing and camera calibration for robotics and embedded systems.
Steps for completing your project
After purchasing the project, send requirements so Pradeeba can start the project.
Delivery time starts when Pradeeba receives requirements from you.
Pradeeba works on your project following the steps below.
Revisions may occur after the delivery date.
Model Design and Integration
Combine a CNN with the YOLO model for facial expression detection.
Model Training
Train the integrated model using a labeled facial expression dataset.