Decision-based neural network for face recognition system

S. Y. Kung, M. Fang, S. P. Liou, M. Y. Chiu, J. S. Taur

Research output: Contribution to conferencePaperpeer-review

14 Scopus citations


This paper proposes a face recognition system based on decision-based neural networks (DBNN). The DBNN adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. The face recognition system consists of three modules. First, a face detector finds the location of a human face in an image. Then an eye localizer determines the positions of both eyes to help generate size-normalized, reoriented, and reduced-resolution feature vectors. (The facial region proposed contains eyebrows, eyes, and nose, but excluding mouth. Eye-glasses will be permissible.) The last module is a face recognizer. The DBNN can be effectively applied to all the three modules. The DBNN based face recognizer has yielded very high recognition accuracies based on experiments on the ARPA-FERET and SCR-IM databases. In terms of processing speeds and recognition accuracies, the performance of DBNN is superior to that of multilayer perceptron (MLP). The training phase for 100 persons would take around one hour, while the recognition phase (including eye localization, feature extraction, and classification using DBNN) consumes only a fraction of a second (on Sparc10).

Original languageEnglish (US)
Number of pages4
StatePublished - Jan 1 1996
EventProceedings of the 1995 IEEE International Conference on Image Processing. Part 3 (of 3) - Washington, DC, USA
Duration: Oct 23 1995Oct 26 1995


OtherProceedings of the 1995 IEEE International Conference on Image Processing. Part 3 (of 3)
CityWashington, DC, USA

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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