This repository is a curated collection of research papers focused on various aspects of face analysis, including detection, alignment, recognition, reconstruction, tracking, and anti-spoofing. It serves as a valuable resource for researchers, engineers, and students in computer vision and machine learning who are working on or interested in face-related technologies.

How It Works

The repository functions as a comprehensive bibliography, primarily sourcing papers from arXiv. It categorizes research across a wide spectrum of face analysis tasks, providing a structured overview of the field. The collection aims to cover the latest advancements and foundational works, enabling users to quickly identify relevant literature for their specific needs.

Quick Start & Requirements

This repository is a collection of research papers and does not have installation or execution requirements. Links to papers and their associated code (where available) are provided within the repository.

Highlighted Details

• Extensive coverage of face detection, alignment, recognition, reconstruction, tracking, generation, transfer, anti-spoofing, and retrieval.
• Includes papers on facial attribute analysis, quality assessment, and model acceleration.
• References other curated lists and resources in the field, providing a broad entry point.
• Papers are primarily sourced from arXiv, ensuring access to recent research.

Maintenance & Community

This is a static collection of papers. There is no active development or community support associated with this repository.

Licensing & Compatibility

The repository itself is a collection of links to research papers. The licensing and compatibility of individual papers are determined by their respective publishers or authors.

Limitations & Caveats

This repository is a bibliography and does not provide any code or implementations. Users must independently access and evaluate the cited research papers. The collection is static and may not reflect the absolute latest publications in real-time.