davidsandberg/facenet
Facenet
FaceNet is a facial recognition framework designed to transform facial images into high-dimensional numerical embeddings for identity verification and recognition. It provides a deep learning face embedder that maps facial features into a Euclidean space where distance corresponds to facial similarity.
The system includes tools for both supervised and unsupervised identity management. It features a face identity classifier for categorizing images into known identity classes and an unsupervised clustering tool to group similar facial embeddings together without predefined labels.
The framework covers a full biometric processing workflow, including facial landmark alignment to standardize image orientation and the training of recognition models. It also includes utilities for measuring model accuracy and tracking performance metrics using benchmark datasets.
Features
- Face Embeddings - Transforms facial images into high-dimensional numerical vectors for identity recognition and clustering.
- Face Alignment Tools - Detects facial landmarks and corrects image orientation to standardize input for recognition models.
- Face Recognition - Categorizes images into known identity classes using trained classification models and facial embeddings.
- Facial Landmark Analysis - Provides facial landmark analysis to standardize image orientation for improved recognition accuracy.
- Facial Recognition - Implements a full system for identifying specific individuals by transforming facial features into embeddings.
- Triplet Margin Losses - Employs triplet margin loss to learn a compact Euclidean space where distance reflects facial similarity.
- Facial Recognition Training - Implements a training pipeline that optimizes neural networks to recognize unique identities across variations in pose and lighting.
- Pre-trained Model Implementations - Uses a pre-trained neural network to extract numerical feature vectors from raw facial pixels.
- Face Embedders - Transforms facial images into high-dimensional vectors for efficient identity verification and comparison.
- Face Clustering - Provides tools for grouping similar facial embeddings to organize image datasets without requiring predefined identity labels.
- Embedding Pairwise Distance Calculators - Calculates the L2 distance between embedding vectors to determine if two faces belong to the same person.
- Head Initialization - Adds a trainable linear output layer to map embeddings to specific identity labels.
- Face Classifiers - Provides a model trained to categorize facial images into known identity classes via numerical embeddings.
- Face Recognition Training - Trains neural networks to map facial images to specific identity classes using labeled datasets.
- Custom Face Classifier Trainers - Trains classification models using labeled image embeddings to recognize specific individuals.
- Image Data Preprocessing - Standardizes facial images through landmark detection and alignment for better model performance.
- K-Means Clustering - Implements K-Means clustering to group unlabeled facial embeddings into clusters of unique individuals.
- Recognition Accuracy Evaluation - Includes utilities for measuring the precision and accuracy of facial recognition models using benchmark datasets.
- Embedding Clusters - Groups similar facial embeddings together to organize images of the same person without requiring labels.
- Model Performance Tracking - Tracks accuracy and validation rates during training to generate performance learning curves for model optimization.
- Computer Vision Libraries - Face recognition implementation using TensorFlow.
- Face Manipulation - Embedding-based face recognition and manipulation.
- Face Recognition - Unified embedding framework for face recognition and clustering.
- Face Recognition and Analysis - System for face identification and similarity clustering.
- Face Recognition Models - Unified embedding framework for face recognition and clustering.
- Face Recognition Research - Unified embedding framework for face recognition and clustering.
