Adaptive classifier integration for invariant face recognition (ACIIFR)

G. A. Khuwaja, M. S. Laghari

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The integration of multiple classifiers promises higher classification accuracy and robustness than can be obtained with a single classifier. We address two problems: (a) automatic recognition of human faces using a novel fusion approach based on an adaptive LVQ network architecture, and (b) improve the face recognition up to 100% while maintaining the learning time per face image constant, which is an scalability issue. The learning time per face image of the recognition system remains constant irrespective of the data size. The integration of the system incorporates the "divide and conquer" modularity principles, i.e. divide the learning data into small modules, train individual modules separately using compact LVQ model structure and still encompass all the variance, and fuse trained modules to achieve recognition rate nearly 100%. The concept of Merged Classes (MCs) is introduced to enhance the accuracy rate. The proposed integrated architecture has shown its feasibility using a collection of 1130 face images of 158 subjects from three standard databases, ORL, PICS and KU. Empirical results yield an accuracy rate of 100% on the face recognition task for 40 subjects in 0.056 seconds per image. Thus, the system has shown potential to be adopted for real time application domains.

Original languageEnglish
Pages (from-to)749-772
Number of pages24
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number6
Publication statusPublished - Sept 2002


  • Adaptive classification
  • Face recognition
  • Integrated classifier
  • Invariant recognition
  • Learning vector quantization
  • Merged class (MC)
  • Pattern recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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