Face identification and verification using PCA and LDA

Lih Heng Chan, Sh Hussain Salleh, Chee Ming Ting, A. K. Ariff

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

12 Citations (Scopus)


Algorithms based on PCA (Principal Components Analysis) and LDA (Linear Discriminant Analysis) are among the most popular appearance-based approaches in face recognition. PCA is recognized as an optimal method to perform dimension reduction, yet being claimed as lacking discrimination ability. LDA once proposed to obtain better classification by using class information. Disputes over the comparison of PCA and LDA have motivated us to study their performance. In this paper, we describe both of these statistical subspace methods and evaluated them using The Database of Faces which comprises 40 subjects with 10 images each. Both identification and verification results have revealed the superiority of LDA over PCA for this medium-sized database.

Original languageEnglish
Title of host publicationProceedings - International Symposium on Information Technology 2008, ITSim
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Print)9781424423286
Publication statusPublished - 2008
Externally publishedYes
EventInternational Symposium on Information Technology 2008 - Kuala Lumpur, Malaysia
Duration: 26 Aug 200829 Aug 2008
https://ieeexplore.ieee.org/xpl/conhome/4625945/proceeding?isnumber=4631524 (Proceedings)

Publication series

NameProceedings - International Symposium on Information Technology 2008, ITSim


ConferenceInternational Symposium on Information Technology 2008
Abbreviated titleITSim 2008
CityKuala Lumpur
Internet address

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