{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"Detects faces in images and extracts facial landmarks (eyes, nose, mouth) using a deep learning model, with optional face alignment for recognition pipelines.","skillfed_tags":["face-detection","computer-vision","deep-learning"],"use_cases":["Preprocessing images for a face recognition system by detecting and aligning faces before passing them to a recognition model.","Extracting facial landmarks for face alignment or geometric analysis in computer vision applications.","Building a face detection API or service that returns bounding boxes and confidence scores for detected faces in bulk image processing.","Integrating face detection into a larger pipeline (e.g., with deepface) for end-to-end face verification or identification tasks.","Analyzing crowd images to locate and extract individual faces for further processing or annotation."],"what_it_does":"RetinaFace is a TensorFlow-based face detection library that identifies faces in images and returns their bounding boxes along with five facial landmarks (both eyes, nose, and mouth corners). It is designed as a preprocessing step for face recognition pipelines, where it can also align detected faces to improve downstream recognition accuracy. The package wraps a re-implementation of the original RetinaFace model from the insightface project, simplifying the API for pip installation while preserving the reference architecture and pre-trained weights.\n\nThe library exposes two main functions: detect_faces() returns face coordinates and landmarks with confidence scores, while extract_faces() crops and optionally aligns detected faces for use in recognition workflows. It depends on numpy, Pillow, opencv-python, and tensorflow, making it suitable for environments where those libraries are already present or acceptable as dependencies.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers low install friction. It is well-suited if you need face detection and landmark extraction as part of a Python computer vision workflow. Install it if you are building a face recognition pipeline or need facial landmarks for alignment; skip it if you only need bounding boxes and can tolerate heavier dependencies like tensorflow."},"id":"retina-face","links":{"html":"https://skillfed.io/packages/retina-face","md":"https://skillfed.io/packages/retina-face.md","pypi":"https://pypi.org/project/retina-face/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-01","license_spdx":null,"license_treatment":"permissive","name":"retina-face","python_support":"supports_current","summary":"RetinaFace: Deep Face Detection Framework in TensorFlow for Python"},"popularity":{"monthly_downloads":219069,"position":9329,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.18"}