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Related Experiment Video

Updated: Jul 5, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

A hierarchical compositional model for face representation and sketching.

Zijian Xu1, Hong Chen, Song-Chun Zhu

  • 1Moody's Corporation, Wall Street Analytics, San Francisco, CA 94111, USA. zjxu@stat.ucla.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 19, 2008
PubMed
Summary

We developed a hierarchical face model using And-Or graphs to capture structural variations. This generative model reconstructs high-resolution faces and creates sketches for applications like face recognition.

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Last Updated: Jul 5, 2026

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Generating realistic facial sketches from high-resolution images is challenging due to complex details and structural variations.
  • Existing face models struggle to account for multi-resolution structural variability.

Purpose of the Study:

  • To present a novel hierarchical-compositional face model using And-Or graphs.
  • To demonstrate the model's capability in reconstructing high-resolution faces and generating sketches.
  • To highlight the model's utility in various computer vision applications.

Main Methods:

  • A three-layer And-Or graph model representing faces hierarchically at different resolutions.
  • Modeling faces as a whole, local parts (templates), and detailed zones (features).
  • Utilizing minimum-description-length for layer transitions and hierarchical dictionaries for diverse representations.

Main Results:

  • Successful reconstruction of high-resolution face images.
  • Generation of cartoon facial sketches.
  • Demonstrated representing power for diverse face structures and features.

Conclusions:

  • The hierarchical-compositional face model effectively addresses structural variabilities across resolutions.
  • The generative model shows promise for applications including face recognition, non-photo-realistic rendering, super-resolution, and face coding.