One-shot Learning and deep face recognition notebooks and workshop materials

Implementation of related angular-margin-based classification loss functions for training (face) embedding models: SphereFace, CosFace, ArcFace and MagFace.

Face things on Android

Face matching using deep learning (CNN embedding + triplet loss)

Andorid library that provide a simple API to compare the similarity between 2 faces from bitmap. This can be implemented to a face recognition system, face authentication, and many more that needed face comparison technique.

API returns face feature vector as a response. This API uses deep learning to generate face embedding 128 dimension vector using Keras on top of TensorFlow. Implementation is "FaceNet: A Unified Embedding for Face Recognition and Clustering". Inception-ResNet-v2 model.

Research SOTA Face Embedding Methods, Face Verification Methods. Apply for Face Verification in Parking App

Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Face Recognition using FaceNet

Real-time face detection and recognition system built from scratch in Python. Utilizes MTCNN for multi-face detection, FaceNet (InceptionResnetV1) for embedding extraction, and Euclidean distance for comparison. Allows training new identities via webcam captures and labels faces as known or “Unknown.”

A deep learning-based face verification system using MTCNN for face detection and InceptionResnetV1 for face recognition, fine-tuned on custom dataset.

FaceNetを用いた顔分類

a lightweight library that identifies individuals in videos by comparing faces against reference images. Leveraging deepface and Docker, it handles video processing, face embedding, matching, and output generation. Enable applications like surveillance and person verification.

This is the documentation regarding the personal project to calculate the value of the average face embedding vector across a large dataset of faces/photos.