Turn images into detailed face masks
Mark attendance using face recognition
Replace faces in images or videos
Upload an image to identify ages, emotions, and genders
Swap faces in videos
Swap faces in videos
This is a face swapper that swaps face within video.
Detect and visualize facial landmarks from a live video feed
Identify people with and without masks in images
extract 68 points landmark from mediapipe-468
Identify faces in photos and label them
Swap faces in videos
Identify and highlight faces in a photo
CelebAMask HQ Face Parsing is a state-of-the-art face recognition tool designed to turn images into detailed face masks. It is built on the CelebA-HQ dataset, which provides high-quality face images, and is optimized for accurate face segmentation. This tool is particularly useful for tasks like facial analysis, image editing, and AI-powered photo enhancements.
• High-Quality Segmentation: Generates detailed face masks with pixel-level accuracy.
• Multiple Labels: Supports segmentation of various facial components such as skin, hair, eyes, eyebrows, nose, mouth, and more.
• Real-Time Processing: Enables quick and efficient face parsing for real-world applications.
• Celebrity-Focused: Tailored for celebrity images but works well on general face datasets.
• Customizable: Allows users to define specific regions of interest for focused processing.
• Research-Grade Accuracy: Built on advanced deep learning architectures for reliable results.
What types of images work best with CelebAMask HQ Face Parsing?
CelebAMask HQ is optimized for high-quality face images, especially those from the CelebA-HQ dataset. However, it works well with most frontal-facing face images.
Can I use CelebAMask HQ for non-celebrity images?
Yes, CelebAMask HQ is designed to work with a wide range of face images, not just celebrities. It provides consistent results across diverse datasets.
How accurate is the face parsing?
CelebAMask HQ achieves state-of-the-art accuracy in facial segmentation tasks, making it suitable for professional and research applications.