vladmandic/human

Features

• Browser-Based Model Inference - Runs machine learning models in the browser or Node.js using GPU acceleration via WebGL or WebGPU.
• Facial Recognition - Generates face embeddings and compares descriptors to identify individuals against a database.
• Computer Vision Libraries - Provides a JavaScript library for face, body, and hand tracking using TensorFlow.js models in the browser or Node.js.
• Object Detection - Identifies and locates non-human objects within images or video streams using machine learning models.
• Biometric Descriptors - Generates numerical biometric descriptors as facial embeddings to enable identity verification via similarity matching.
• Face Detection - Locates faces within images or video streams to enable targeted analysis of facial features.
• Liveness Detectors - Analyzes facial attributes and movements to verify a live human presence and prevent spoofing.
• Face Embeddings - Extracts unique numerical descriptors from face images to represent facial features for biometric comparison.
• GPU-Accelerated Inference - Accelerates machine learning computations by utilizing GPU drivers and graphics APIs for faster inference.
• Whole-Body - Simultaneously detects 3D landmarks for the body, face, and hands to recognize gestures and posture.
• State-Based Landmark Tracking - Maps 3D keypoints for the head, body, and hands to analyze gestures and orientation across video frames.
• Full Body Capture - Performs comprehensive 3D tracking of the head, face, eyes, body, and hands for virtual model animation.
• Background Segmentation - Separates humans from their backgrounds to produce foreground-only or alpha-channel images.
• Facial Similarity Matching - Compares an input image against a set of known faces to retrieve the most similar person.
• Video Input Processing - Provides real-time capture and streaming of live video frames from devices for AI-based visual analysis.
• Human Pose Detections - Identifies human faces, body skeletons, and hand gestures in real-time video frames.
• Face Tracking - Tracks 3D face rotation and iris movements to determine gaze and head orientation.
• Detection-to-Track Association - Implements logic to associate detected body parts and features with specific individuals for consistent tracking.
• Capture Readiness Validation - Analyzes camera input against required tests to ensure a face is properly positioned before capturing.
• Gesture Recognition Libraries - Processes landmark coordinates to identify and classify specific human gestures in real-time.
• Detection Visualization - Renders detected faces, bodies, hands, and objects as visual overlays on a canvas for verification.
• Facial Analysis Toolkits - Predicts demographic and emotional attributes, such as age, gender, and emotion, from facial features.
• Hardware-Agnostic Inference Switching - Optimizes inference speed and latency by automatically switching between CPU and GPU runtimes based on availability.
• Iris Tracking - Provides iris movement monitoring to determine gaze direction and eye orientation.
• Model Execution Environments - Provides a CPU-based execution environment for running inference in systems lacking dedicated graphics hardware.
• Model Output Visualizers - Renders detection results as points, outlines, or 3D meshes to visually represent features.
• Temporal Prediction Smoothing - Uses temporal prediction smoothing to reduce coordinate jitter and ensure fluid movement of detected landmarks across frames.
• Inference Optimizers - Optimizes inference speed by selecting the fastest available hardware backend and implementing model caching.
• Canvas Rendering - Provides the ability to render 3D meshes and detection overlays directly onto an HTML5 canvas.
• Cross-Platform Media Frameworks - Analyzes images and videos across browser and server runtimes using native methods or external tools.
• Media Analysis - Analyzes visual data from images, canvases, webcams, and various video streaming formats.
• Temporal Interpolation - Generates smooth transitions between consecutive detection data points to reduce visual jitter across frames.
• Coordinate Smoothing - Blends consecutive detection results to eliminate jumping and ensure fluid visual updates of tracked landmarks.
• Asynchronous Processing Pipelines - Implements asynchronous pipelines to handle heavy model execution in background threads, maintaining browser responsiveness.
• Main Thread Offloading - Offloads computationally expensive vision analysis to background web workers to prevent blocking the main UI thread.

Star history