Search Results for "face recognition using python"
Sort By:
Showing 132 open source projects for "face recognition using python"
View related business solutions-
Keep company data safe with Chrome EnterpriseMake AI work your way with Chrome Enterprise. Block unapproved sites and set custom data controls that align with your company's policies.
-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
1
OpenFace Face Recognition
Face recognition with deep neural networks
OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. -
2
InsightFace
State-of-the-art 2D and 3D Face Analysis Project
State-of-the-art deep face analysis library. InsightFace is an open-source 2D&3D deep face analysis library. InsightFace is an integrated Python library for 2D&3D face analysis. InsightFace efficiently implements a wide variety of state-of-the-art algorithms for face recognition, face detection, and face alignment, which are optimized for both training and deployment. -
3
deepface
A Lightweight Face Recognition and Facial Attribute Analysis
DeepFace is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, ArcFace, Dlib, SFace and GhostFaceNet. Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level. -
4
Face Recognition Software to compare 2 pictures of person to make sure that he/she is the same person. Using deepface library at python. Requirement. • Java •Python •Library Deepface from Python How to use? - Input the picture 1/Gambar 1 - Input the picture 2/Gambar 2 - Click Analyze and then the machine Will process compare the face
-
Gen AI apps are built with MongoDB AtlasMongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
-
5
Deep-Live-Cam
Real time face swap and one-click video deepfake
Real time face swap and one-click video deepfake with only a single image. Choose a face (image with the desired face) and the target image/video (image/video in which you want to replace the face) and click on Start. Open File Explorer and navigate to the directory you select your output to be in. You will find a directory named <video_title> where you can see the frames being swapped in real time. Once the processing is done, it will create the output file. -
6
Face Alignment
2D and 3D Face alignment library build using pytorch
Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates. Build using FAN's state-of-the-art deep learning-based face alignment method. For numerical evaluations, it is highly recommended to use the lua version which uses identical models with the ones evaluated in the paper. -
7
Liveliness and Face Identification
Leading free and open-source liveliness check &face recognition system
This is a free and open-source liveliness check and face recognition project. Essentially, it is an application that can be used as a standalone server or deployed in the cloud. You don’t need prior machine learning skills to set up and use. The application is customizable react based mobile friendly UI and Python based backend. The program is a real-time face detection application. It allows you to detect faces using your webcam and displays the video feed with oval drawn around the detected faces. ... -
8
Qwen2-Audio
Repo of Qwen2-Audio chat & pretrained large audio language model
Qwen2-Audio is a large audio-language model by Alibaba Cloud, part of the Qwen series. It is trained to accept various audio signal inputs (including speech, sounds, etc.) and perform both voice chat and audio analysis, producing textual responses. It supports two major modes: Voice Chat (interactive voice only input) and Audio Analysis (audio + text instructions), with both base and instruction-tuned models. It is evaluated on many benchmarks (speech recognition, translation, sound... -
9
sherpa-onnx
Speech-to-text, text-to-speech, and speaker recognition
Speech-to-text, text-to-speech, and speaker recognition using next-gen Kaldi with onnxruntime without an Internet connection. Support embedded systems, Android, iOS, Raspberry Pi, RISC-V, x86_64 servers, websocket server/client, C/C++, Python, Kotlin, C#, Go, NodeJS, Java, Swift, Dart, JavaScript, Flutter. -
Case Management Software for NonprofitsA case management CRM that focuses on your clients, to measure real-world impact, not just data. Your Day Just Got Shorter - We know you’re busy and don’t have time for complicated software – We’ve changed that! Even your staff will enjoy using the system – No more headaches and missing data from reports so you can use your valuable time to serve more people. CharityTracker is a cloud-based, HIPAA compliant software that’s easy to use for nonprofits of all sizes. With no setup fees, hidden fees, or contracts, you can cancel anytime! Our time saving solution provides a secure way to streamline how you manage cases, record case notes & assistance, create reports, collaborate, set goals, measure outcomes, make referrals & prove your impact with change-over-time reports for your funders. We're at work in 2,200 communities! Book a free demo. Pricing starts at just $20/mo. user
-
10
Deface GUI - Face Anonymization Tool
Graphical User Interface Face Anonymization Tool
This application is a professional tool with a graphical user interface that enables anonymization of faces using the Deface Engine. Cross-Platform Compatible (Linux-Windows) NOTE: To use on Windows, first install Python. Then, if necessary, install “pip install deface” (only if necessary). -
11
Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that... -
12
DocTR
Library for OCR-related tasks powered by Deep Learning
DocTR provides an easy and powerful way to extract valuable information from your documents. Seemlessly process documents for Natural Language Understanding tasks: we provide OCR predictors to parse textual information (localize and identify each word) from your documents. Robust 2-stage (detection + recognition) OCR predictors with pretrained parameters. User-friendly, 3 lines of code to load a document and extract text with a predictor. State-of-the-art performances on public document... -
13
MediaPipe Face Detection
Detect faces in an image
The MediaPipe Face Detection model is a high-performance, real-time face detection solution that uses machine learning to identify faces in images and video streams. It is optimized for mobile and embedded platforms, offering fast and accurate face detection while maintaining a small memory footprint. This model supports multiple face detections and is highly efficient, making it suitable for a variety of applications such as augmented reality, user authentication, and facial expression analysis. -
14
Omnilingual ASR
Omnilingual ASR Open-Source Multilingual SpeechRecognition
Omnilingual-ASR is a research codebase exploring automatic speech recognition that generalizes across a very large number of languages using shared modeling and training recipes. It focuses on leveraging self-supervised audio pretraining and scalable fine-tuning so low-resource languages can benefit from high-resource data. The project provides data preparation pipelines, training scripts, decoding utilities, and evaluation tools so researchers can reproduce results and extend to new... -
15
spacy-llm
Integrating LLMs into structured NLP pipelines
Large Language Models (LLMs) feature powerful natural language understanding capabilities. With only a few (and sometimes no) examples, an LLM can be prompted to perform custom NLP tasks such as text categorization, named entity recognition, coreference resolution, information extraction and more. This package integrates Large Language Models (LLMs) into spaCy, featuring a modular system for fast prototyping and prompting, and turning unstructured responses into robust outputs for various... -
16
thumbor
An open-source photo thumbnail service by globo.com
Open-source smart on-demand image cropping, resizing and filters. Thumbor is a smart imaging service. It enables on-demand crop, resizing and flipping of images. It features a very smart detection of important points in the image for better cropping and resizing, using state-of-the-art face and feature detection algorithms (more on that in Detection Algorithms). Save time and money in your company with Thumbor. With the most advanced face detection technology, Thumbor ensure you to have the... -
17
DeepSeek MoE
Towards Ultimate Expert Specialization in Mixture-of-Experts Language
DeepSeek-MoE (“DeepSeek MoE”) is the DeepSeek open implementation of a Mixture-of-Experts (MoE) model architecture meant to increase parameter efficiency by activating only a subset of “expert” submodules per input. The repository introduces fine-grained expert segmentation and shared expert isolation to improve specialization while controlling compute cost. For example, their MoE variant with 16.4B parameters claims comparable or better performance to standard dense models like DeepSeek 7B... -
18
LLaMA-Factory
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
LLaMA-Factory is a fine-tuning and training framework for Meta's LLaMA language models. It enables researchers and developers to train and customize LLaMA models efficiently using advanced optimization techniques. -
19
scikit-image
Image processing in Python
scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. scikit-image builds on scipy.ndimage to provide a versatile set of image processing routines in Python. This library is developed by its community, and contributions are most welcome! Read about our mission, vision, and values and how we govern the project. Major proposals... -
20
Xorbits Inference
Replace OpenAI GPT with another LLM in your app
Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop. Xorbits Inference(Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. With Xorbits... -
21
SAM 3D Body
Code for running inference with the SAM 3D Body Model 3DB
...The repository provides Python code to run inference, utilities to download checkpoints from Hugging Face, and demo scripts that turn images into 3D meshes and visualizations. There are Jupyter notebooks that walk you through setting up the model, running it on example images, and visualizing outputs in 3D, making it approachable even if you are not a 3D expert. -
22
SoniTranslate
Synchronized Translation for Videos
SoniTranslate is a video translation and dubbing system that produces synchronized target-language audio tracks for existing video content. It provides a web UI built with Gradio, allowing users to upload a video, choose source and target languages, and then run a pipeline that handles transcription, translation and re-synthesis of speech. Under the hood, it uses advanced speech and diarization models to separate speakers, align audio with timecodes and respect subtitle timing, which lets... -
23
Qwen2.5-Omni
Capable of understanding text, audio, vision, video
Qwen2.5-Omni is an end-to-end multimodal flagship model in the Qwen series by Alibaba Cloud, designed to process multiple modalities (text, images, audio, video) and generate responses both as text and natural speech in streaming real-time. It supports “Thinker-Talker” architecture, and introduces innovations for aligning modalities over time (for example synchronizing video/audio), robust speech generation, and low-VRAM/quantized versions to make usage more accessible. It holds... -
24
Airtest
UI Automation Framework for Games and Apps
¿Airtest provides cross-platform APIs, including app installation, simulated input, assertion and so forth. Airtest uses image recognition technology to locate UI elements so that you can automate games and apps without injecting any code. Airtest cases can be easily run on large device farms, using the command line or python API. HTML reports with detailed info and screen recording allow you to quickly locate failure points. NetEase builds Airlab on top of the Airtest Project. ... -
25
Transformers
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX
...Images, for tasks like image classification, object detection, and segmentation. Audio, for tasks like speech recognition and audio classification. Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.
