Overlapping local phase feature (OLPF) for robust face recognition in surveillance

  • Authors:

  • Qiang Liu;King Ngi Ngan

  • Affiliations:

  • Dept. of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong;Dept. of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong

  • Venue:
  • ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

As a non-invasive biometric method, face recognition in surveillance is a very challenging problem because of the concurrence of conditions, such as under the variable illumination with uncontrolled pose and movement in low-resolution of subject. In this paper, we present a robust human face recognition system for surveillance. Unlike traditional recognition system which detect face region directly, we use a Cascade Head-Shoulder Detector (CHSD) and a trained human body model to find the face region in an image. To recognize human face, an efficient feature, Overlapping Local Phase Feature (OLPF), is proposed, which is robust to pose and blurring without adversely affecting discrimination performance. To describe the variations of faces, Adaptive Gaussian Mixture Model (AGMM) is proposed which can describe the distributions of the face images. Since AGMM does not need the topology of face, the proposed method is resistant to the face detection errors caused by wrong or no alignment. Experimental results demonstrate the robustness of our method on public dataset as well as real data from surveillance camera.