Practical overview
When it fits
Use Deepface when a Python application needs higher-level face-recognition or facial-attribute functions while the library manages common detection, alignment, normalization, representation and verification stages.
Who it is for
- Python developers building computer-vision applications
- Teams integrating face verification or identity search into self-hosted services
- Researchers and prototypers exploring facial attribute analysis
Documented uses
- Compare two facial images to determine whether they represent the same person.
- Search a specified folder, S3 location or FTP service for the identity represented by an input image.
- Add age, gender, emotion or race analysis to an image-processing workflow.
- Connect recognition workflows with supported database and vector-search backends.
Requirements & limits
The public repository identifies the project as MIT licensed. Installation is documented through PyPI or from the repository source.
- The project supports TensorFlow, PyTorch and ONNX Runtime installation paths.
- The library wraps multiple recognition models, including VGG-Face, FaceNet, ArcFace, Dlib, SFace, GhostFaceNet and Buffalo_L.
- Identity search can target a specified folder, S3 or FTP location.
- Database-backed operations are documented with PostgreSQL, MongoDB, Neo4j, pgvector and Pinecone integrations.
- The source tree may contain features not yet published in a PyPI release.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Deepface with a selected Python backend
Set up the Deepface library and import it into a Python project.
Before you start
- Python environment
- A choice of TensorFlow, PyTorch or ONNX Runtime backend
- PyPI access or a clone of the public repository
Steps
Install Deepface from PyPI with the TensorFlow, PyTorch or ONNX extra, or clone the repository and install its source tree with one of those extras.
Manage the backend choice when multiple frameworks are installed by setting the documented Deepface backend engine option.
Import the library with
from deepface import DeepFace.
Expected outcome
A Python environment containing Deepface and a selected deep-learning backend, ready for the library functions described by the project.
Guide limitations
- This setup recipe does not establish that a particular operating-system, model or backend combination has been tested.
- The project also documents a managed API for users who do not want to manage infrastructure, but this recipe covers the library installation path.
Compare two facial images with Deepface
Prepare the documented face-verification call that returns whether two images belong to the same person.
Before you start
- A Python environment with Deepface installed
- Two facial image paths
Steps
Install Deepface and import
DeepFace.Pass the two image paths to
DeepFace.verify.Read the returned dictionary's
verifiedvalue:Trueindicates the images are of the same person andFalseindicates different people.
Expected outcome
A verification result dictionary containing the documented boolean identity comparison result.
Guide limitations
- The source describes the function and result semantics but does not provide a performance, accuracy or production-safety guarantee for a particular dataset.
- This guide covers image comparison, not identity search or database-backed recognition.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 1 October 2026
- Original website title
- serengil/deepface · Official homepage title · retained in its source language