Fast Haar transform based feature extraction for face representation and recognition

Yanwei Pang*, Xuelong Li, Yuan Yuan, Dacheng Tao, Jing Pan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency.

Original languageEnglish
Pages (from-to)441-450
Number of pages10
JournalIEEE Transactions on Information Forensics and Security
Issue number3
Early online date7 Jul 2009
Publication statusPublished - Sept 2009


  • face representation and recognition
  • fast algorithm
  • feature extraction
  • Haar transform
  • subspace analysis


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