Deepfakes Software For All
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Updated
Jul 10, 2024 - Python
Deepfakes Software For All
DeepFaceLab is the leading software for creating deepfakes.
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
DeepNude's algorithm and general image generation theory and practice research, including pix2pix, CycleGAN, UGATIT, DCGAN, SinGAN, ALAE, mGANprior, StarGAN-v2 and VAE models (TensorFlow2 implementation). DeepNude的算法以及通用生成对抗网络(GAN,Generative Adversarial Network)图像生成的理论与实践研究。
Keras implementation of the renowned publication "DeepFace: Closing the Gap to Human-Level Performance in Face Verification" by Taigman et al. Pre-trained weights on VGGFace2 dataset.
face detection, verification and recognition using Keras
Facial Emotion Recognition using OpenCV and Deepface
即時人臉辨識(使用OpenCV與FaceNet)
A Streamlit web application for face recognition using a pre-trained YOLO model and the DeepFace library.
This project is a comprehensive face recognition-based attendance system for universities. It leverages OpenCV for face detection and recognition, Firebase for data storage, and Flask for the web interface. The system allows for student registration, face capture, and attendance tracking, providing a modern solution for attendance management.
This is a lightweight, easy to use GUI Toolbox for DeepFace, a face recognition framework that could analyze age, gender, emotion, race, and verify whether two faces are similar.
Python Real Time Face Detection
recops is a facial analysis framework, an AI forensic toolkit designed specifically for visual investigations and analysis workflows in OSINT research.
Introducing the faster and more optimized version of DeepFaceLab.
A multimodal face liveness detection module that can be used in the context of face anti-spoofing
This application explain how we can easily integrate Deepface framework with Python Django application
This project implements real-time facial emotion detection using the deepface library and OpenCV. It captures video from the webcam, detects faces, and predicts the emotions associated with each face. The emotion labels are displayed on the frames in real-time.
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