Steerable pyramid-based face hallucination

  • Authors:

  • Congyong Su;Yueting Zhuang;Li Huang;Fei Wu

  • Affiliations:

  • College of Computer Science, Zhejiang University, 310027 Hangzhou, China and Microsoft Visual Perception Laboratory of Zhejiang University, 310027 Hangzhou, China;College of Computer Science, Zhejiang University, 310027 Hangzhou, China and Microsoft Visual Perception Laboratory of Zhejiang University, 310027 Hangzhou, China;College of Computer Science, Zhejiang University, 310027 Hangzhou, China and Microsoft Visual Perception Laboratory of Zhejiang University, 310027 Hangzhou, China;College of Computer Science, Zhejiang University, 310027 Hangzhou, China and Microsoft Visual Perception Laboratory of Zhejiang University, 310027 Hangzhou, China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2005

Quantified Score

Hi-index 0.01

Visualization

Abstract

In this paper we propose a robust learning-based face hallucination algorithm, which predicts a high-resolution face image from an input low-resolution image. It can be utilized for many computer vision tasks, such as face recognition and face tracking. With the help of a database of other high-resolution face images, we use a steerable pyramid to extract multi-orientation and multi-scale information of local low-level facial features both from the input low-resolution face image and other high-resolution ones, and use a pyramid-like parent structure and local best match approach to estimate the best prior; then, this prior is incorporated into a Bayesian maximum a posterior (MAP) framework, and finally the high-resolution version is optimized by a steepest decent algorithm. The experimental results show that we can enhance a 24x32 face image into a 96x128 one while the visual effect is relatively good.