{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"DeepFace performs face recognition, verification, and facial attribute analysis (age, gender, emotion, race) using deep learning models, with support for real-time video streams and large-scale database searches.","skillfed_tags":["face-recognition","computer-vision","deep-learning"],"use_cases":["Verify whether two photos belong to the same person for identity confirmation or fraud detection workflows.","Search a database of employee or customer photos to identify individuals in new images for security or CRM applications.","Analyze facial attributes (age, gender, emotion, race) in batch or real-time video for demographic insights or sentiment analysis.","Build a real-time video stream analyzer that continuously detects and identifies faces from a webcam feed.","Register and index face embeddings in a vector database for large-scale face recognition at scale."],"what_it_does":"DeepFace is a Python framework that wraps multiple state-of-the-art face recognition and analysis models (VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, ArcFace, Dlib, SFace, GhostFaceNet, Buffalo_L) into a unified API. It handles the full face recognition pipeline\u2014detection, alignment, normalization, representation, and verification\u2014internally, so you can call high-level functions like `verify()`, `find()`, or `analyze()` with minimal setup.\n\nThe package supports three main workflows: one-to-one face verification (comparing two images), one-to-many face recognition (searching a database of embeddings stored in PostgreSQL, MongoDB, Neo4j, Pinecone, pgvector, or Weaviate), and facial attribute analysis (predicting age, gender, emotion, and race from images). It also includes real-time video streaming for continuous analysis. With 18 runtime dependencies including TensorFlow, Keras, OpenCV, and Flask, it brings substantial computational machinery but abstracts away the complexity of model selection and pipeline orchestration.","worth_installing":"Yes, if you need face recognition or facial attribute analysis and can accept the substantial dependency footprint (TensorFlow, Keras, OpenCV). The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and abstracts away significant complexity. Install it if your use case justifies the disk and memory overhead; avoid it if you need a lightweight, minimal-dependency solution or are uncertain whether you need deep learning-based face analysis."},"id":"deepface","links":{"html":"https://skillfed.io/packages/deepface","md":"https://skillfed.io/packages/deepface.md","pypi":"https://pypi.org/project/deepface/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-09","license_spdx":null,"license_treatment":"permissive","name":"deepface","python_support":"supports_current","summary":"A Lightweight Face Recognition and Facial Attribute Analysis Framework (Age, Gender, Emotion, Race) for Python"},"popularity":{"monthly_downloads":587559,"position":5874,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.100"}