{"name":"deepface: Lightweight Face Recognition and Facial Attribute Analysis Library","description":"deepface is a powerful yet lightweight Python library for face recognition and facial attribute analysis. It offers capabilities for age, gender, emotion, and race prediction, wrapping state-of-the-art models for robust performance. Developers can easily integrate advanced facial analysis into their applications with just a few lines of code.","github":"https://github.com/serengil/deepface","url":"https://osrepos.com/repo/serengil-deepface","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/serengil-deepface","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/serengil-deepface.md","json":"https://osrepos.com/repo/serengil-deepface.json","topics":["Python","Face Recognition","Deep Learning","Facial Analysis","Machine Learning","Computer Vision","Emotion Recognition","Age Prediction"],"keywords":["Python","Face Recognition","Deep Learning","Facial Analysis","Machine Learning","Computer Vision","Emotion Recognition","Age Prediction"],"stars":null,"summary":"deepface is a powerful yet lightweight Python library for face recognition and facial attribute analysis. It offers capabilities for age, gender, emotion, and race prediction, wrapping state-of-the-art models for robust performance. Developers can easily integrate advanced facial analysis into their applications with just a few lines of code.","content":"## Introduction\ndeepface is a robust and lightweight Python library designed for advanced face recognition and comprehensive facial attribute analysis. It empowers developers to easily implement functionalities like age, gender, emotion, and race prediction. This hybrid framework integrates and wraps several state-of-the-art deep learning models, including VGG-Face, FaceNet, OpenFace, ArcFace, and DeepFace, ensuring high accuracy and flexibility. It simplifies the complex pipeline of face detection, alignment, normalization, representation, and verification, allowing users to perform sophisticated facial analysis with minimal code.\n\n## Installation\nGetting started with deepface is straightforward. The easiest way to install the library and its prerequisites is via pip:\n\nshell\n$ pip install deepface\n\n\nAlternatively, you can install it from the source code to access the latest features:\n\nshell\n$ git clone https://github.com/serengil/deepface.git\n$ cd deepface\n$ pip install -e .\n\n\nOnce installed, you can import and use deepface functionalities:\n\npython\nfrom deepface import DeepFace\n\n\n## Examples\ndeepface provides a simple API for various facial analysis tasks:\n\n## Face Verification\nDetermine if two facial images belong to the same person. The `verify` function returns a dictionary, with `verified` indicating the result and a `confidence` score.\n\npython\nresult = DeepFace.verify(img1_path = \"img1.jpg\", img2_path = \"img2.jpg\")\n\n\n## Face Recognition\nIdentify a person from a database of faces. The `find` function searches for the identity of an input image within a specified database path, returning a list of pandas DataFrames.\n\npython\ndfs = DeepFace.find(img_path = \"img1.jpg\", db_path = \"C:/my_db\")\n\n\n## Facial Attribute Analysis\nAnalyze facial attributes such as age, gender, emotion, and race. The `analyze` function provides detailed predictions for each detected face.\n\npython\nobjs = DeepFace.analyze(\n  img_path = \"img4.jpg\", actions = ['age', 'gender', 'race', 'emotion']\n)\n\n\n## Real-Time Analysis\nPerform real-time face recognition and attribute analysis using a webcam. The `stream` function accesses your webcam and processes frames sequentially.\n\npython\nDeepFace.stream(db_path = \"C:/database\")\n\n\n## Embeddings\nExtract multi-dimensional vector embeddings from facial images. These embeddings are crucial for similarity comparisons and can be obtained using the `represent` function.\n\npython\nembedding_objs = DeepFace.represent(img_path = \"img.jpg\")\n\n\n## Why Use deepface\ndeepface stands out for its comprehensive feature set and ease of use. It wraps numerous state-of-the-art face recognition models and detection backends, offering unparalleled flexibility and performance. Whether you need to verify identities, recognize faces in a database, or analyze facial attributes in real-time, deepface provides a robust and efficient solution. Its modular design allows for customization of models, detectors, and distance metrics, making it adaptable to various application requirements, from small-scale projects to large-scale facial recognition systems with vector databases and homomorphic encryption.\n\n## Links\nExplore deepface further through these official resources:\n* [GitHub Repository](https://github.com/serengil/deepface){:target=\"_blank\"}\n* [PyPI Package](https://pypi.org/project/deepface/){:target=\"_blank\"}\n* [Official Blog (sefiks.com)](https://sefiks.com){:target=\"_blank\"}","metrics":{"detailViews":4,"githubClicks":11},"dates":{"published":null,"modified":"2025-11-20T16:00:49.000Z"}}