A discriminant pseudo zernike moments in face recognition

Ying Han Pang, Andrew B.J. Teoh, David C.L. Ngo

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


This paper introduces a novel discriminant moment-based method as a feature extraction technique for face recognition. In this method, pseudo Zernike moments are performed before the application of Fisher's Linear Discriminant to achieve a stable numerical computation and good generalization in small-sample-size problems. Fisher's Linear Discriminant uses pseudo Zernike moments to derive an enhanced subset of moment features by maximizing the between-class scatter, while minimizing the within-class scatter, which leads to a better discrimination and classification performance. Experimental results show that the proposed method achieves superior performance with a recognition rate of 97.51% in noise free environment and 97.12% in noise induced environment for the Essex Face94 database. For the Essex Face95 database, the recognition rates obtained are 91.73% and 90.30% in noise free and noise induced environments, respectively.

Original languageEnglish
Pages (from-to)197-211
Number of pages15
JournalJournal of Research and Practice in Information Technology
Issue number2
Publication statusPublished - 2006

All Science Journal Classification (ASJC) codes

  • Software
  • Management Information Systems
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications


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