PCA and LDA-based face verification using back-propagation neural network

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

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

7 Citations (Scopus)


In this paper, we present back-propagation neural network (BPNN) as back-end classifier for face verification. Face features are extracted based on principal component analysis (peA) and linear discriminant analysis (LDA). peA efficiently reduces dimension of face images and represent them with eigenfaces; while LDA is alternatively used to improve discriminant ability of the peA algorithm. Backpropagation neural network (BPNN) is used to learn the patterns of peA and LDA features and produce relevant client and imposter scores for verification. The algorithms were evaluated using AT&T face database which comprises 40 subjects and with a total size of 400 images. Experimental results show that BPNN significantly improves the performance of face verification which is based on Euclidean distance. Percentages of improvement in equal error rate (EER) by range 62%-85% is achieved by BPNN.

Original languageEnglish
Title of host publication10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
Number of pages5
Publication statusPublished - 2010
Externally publishedYes
EventInternational Conference on Information Science, Signal Processing and their Applications 2010 - Kuala Lumpur, Malaysia
Duration: 10 May 201013 May 2010
Conference number: 10th
https://ieeexplore.ieee.org/xpl/conhome/5605286/proceeding (Proceedings)


ConferenceInternational Conference on Information Science, Signal Processing and their Applications 2010
Abbreviated titleISSPA 2010
CityKuala Lumpur
Internet address


  • Artificial neural network
  • Face verification
  • Linear discriminant analysis

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