# deepface: Lightweight Face Recognition and Facial Attribute Analysis Library

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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
OSRepos URL: https://osrepos.com/repo/serengil-deepface

## 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.

## Topics

- Python
- Face Recognition
- Deep Learning
- Facial Analysis
- Machine Learning
- Computer Vision
- Emotion Recognition
- Age Prediction

## Repository Information

Last analyzed by OSRepos: Thu Nov 20 2025 16:00:49 GMT+0000 (Western European Standard Time)
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## Content

## Introduction
deepface 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.

## Installation
Getting started with deepface is straightforward. The easiest way to install the library and its prerequisites is via pip:

shell
$ pip install deepface


Alternatively, you can install it from the source code to access the latest features:

shell
$ git clone https://github.com/serengil/deepface.git
$ cd deepface
$ pip install -e .


Once installed, you can import and use deepface functionalities:

python
from deepface import DeepFace


## Examples
deepface provides a simple API for various facial analysis tasks:

## Face Verification
Determine if two facial images belong to the same person. The `verify` function returns a dictionary, with `verified` indicating the result and a `confidence` score.

python
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")


## Face Recognition
Identify 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.

python
dfs = DeepFace.find(img_path = "img1.jpg", db_path = "C:/my_db")


## Facial Attribute Analysis
Analyze facial attributes such as age, gender, emotion, and race. The `analyze` function provides detailed predictions for each detected face.

python
objs = DeepFace.analyze(
  img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion']
)


## Real-Time Analysis
Perform real-time face recognition and attribute analysis using a webcam. The `stream` function accesses your webcam and processes frames sequentially.

python
DeepFace.stream(db_path = "C:/database")


## Embeddings
Extract multi-dimensional vector embeddings from facial images. These embeddings are crucial for similarity comparisons and can be obtained using the `represent` function.

python
embedding_objs = DeepFace.represent(img_path = "img.jpg")


## Why Use deepface
deepface 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.

## Links
Explore deepface further through these official resources:
* [GitHub Repository](https://github.com/serengil/deepface){:target="_blank"}
* [PyPI Package](https://pypi.org/project/deepface/){:target="_blank"}
* [Official Blog (sefiks.com)](https://sefiks.com){:target="_blank"}