Identify and align faces in a given image
Display face recordings from images
Identify celebrities in images
Detect facial expressions in images
Swap faces in images or videos
Mark attendance using face recognition
Swap faces in a video
extract 68 points landmark from mediapipe-468
2 head pose estimation with mediapipe and trained-model
Identify and match faces from webcam input
Recognize facial expressions from images
3D Passive Face Liveness Detection (Face Anti-Spoofing)
Recognize emotions in images and videos
Atksh Onnx Facial Lmk Detector is a powerful tool designed for facial landmark detection in images. It leverages the ONNX (Open Neural Network Exchange) format to deliver efficient and accurate face alignment and feature extraction. This tool is ideal for applications requiring precise facial analysis, such as face recognition, facial expressions analysis, and augmented reality.
• High Accuracy: Utilizes advanced neural networks to detect facial landmarks with precision.
• Real-Time Detection: Optimized for fast processing, enabling real-time applications.
• Multi-Face Support: Capable of detecting landmarks for multiple faces in a single image.
• Lightweight: Built with ONNX for reduced computational requirements.
• Cross-Platform Compatibility: Supports integration with various platforms, including Python and mobile devices.
pip install atksh-onnx-facial-lmk-detector.from atksh_onnx import FacialLandmarkDetector.detector = FacialLandmarkDetector().landmarks = detector.detect(image).What is the difference between Atksh Onnx Facial Lmk Detector and other facial landmark detectors?
Atksh Onnx Facial Lmk Detector uses the ONNX format, which ensures better performance and compatibility across different platforms while maintaining high accuracy.
Can I use Atksh Onnx Facial Lmk Detector for real-time video processing?
Yes, the detector is optimized for real-time processing, making it suitable for video analysis and live applications.
What output format does the detector provide for facial landmarks?
The detector outputs facial landmarks as a set of coordinates, typically in the form of (x, y) points for each key facial feature.