What is awesome-face_recognition?

chanchichoi/awesome-face_recognition — explained in plain English

Analysis updated 2026-06-26

★ 4,740Audience · researcherComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((repo))
    What it does
      Curated paper list
      Face research topics
      Chronological links
    Topics Covered
      Face detection
      Recognition and verification
      3D reconstruction
    More Topics
      Face generation
      Attribute analysis
      Anti-spoofing
    Content Format
      Paper titles and authors
      Arxiv links
      Benchmark datasets
    Audience
      Researchers
      PhD students
      CV practitioners

mindmap root((repo)) What it does Curated paper list Face research topics Chronological links Topics Covered Face detection Recognition and verification 3D reconstruction More Topics Face generation Attribute analysis Anti-spoofing Content Format Paper titles and authors Arxiv links Benchmark datasets Audience Researchers PhD students CV practitioners

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What do people build with it?

USE CASE 1

Find landmark academic papers on face detection or recognition as a starting point for a literature review.

USE CASE 2

Discover benchmark datasets used in face recognition research to evaluate your own models.

USE CASE 3

Trace how face generation or anti-spoofing research has evolved chronologically from 2008 to recent years.

USE CASE 4

Identify key authors and papers in a specific sub-topic like facial landmark alignment or face super-resolution.

How does it compare?

	chanchichoi/awesome-face_recognition	adongwanai/agentguide	bing-su/adetailer
Stars	4,740	4,740	4,740
Language	—	HTML	Python
Setup difficulty	easy	moderate	easy
Complexity	1/5	3/5	2/5
Audience	researcher	developer	designer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

No installation needed, browse the README on GitHub. Follow arxiv links to read papers. Find code implementations on authors' pages or in separate repositories.

Curated reading list, no software license applies. Paper links point to external publications.

So what is it?

This repository is a curated reading list of academic research papers about computer vision techniques related to faces. It covers a broad range of face-related tasks that researchers study: detecting where faces appear in an image, aligning facial landmarks, recognizing or verifying specific people, analyzing facial attributes, reconstructing faces in 3D, tracking faces across video frames, enhancing low-resolution face images, generating synthetic faces, editing or transferring face appearance, and detecting attempts to fool face recognition systems with photos or masks. The collection is organized chronologically within each topic, going back to papers from around 2008 and continuing through recent years. Each entry lists the paper title, authors, and a link to the arxiv preprint or the original publication. There are no code implementations or tutorials here, just links to the papers themselves. The list draws from several other similar curated collections on GitHub, which are credited in the introduction. It also includes a datasets section pointing to benchmark datasets used in face recognition research. The intended audience is researchers or students who want a comprehensive starting point for reading the academic literature on face-related computer vision. Someone looking to build a face recognition application would need to go from these paper titles to the actual implementations, which are usually found on the authors' own pages or in separate code repositories. The list is not a tutorial on how face recognition works but rather a bibliography for people already familiar with the research field. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

I'm writing a literature review on face recognition. Based on the awesome-face_recognition list, what are the most cited or foundational papers I should start with?

Prompt 2

I want to understand how face anti-spoofing (detecting fake faces or masks) has developed over time. Summarise the progression of approaches from the papers listed in this repo.

Prompt 3

From the datasets section of awesome-face_recognition, which benchmark datasets are most commonly used for evaluating face verification models?

Prompt 4

I'm building a face detection module. Which papers in this list should I read first to understand the main detector architectures used in practice?

Prompt 5

Explain in plain English what the difference is between face recognition and face verification, and point me to papers in this list that cover each task.

Frequently asked questions