Face recognition using the second-order mixture-of-eigenfaces method

Hyun Chul Kim, Daijin Kim, Sung Yang Bang, Sang Youn Lee

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


The well-known eigenface method uses an eigenface set obtained from principal component analysis. However, the single eigenface set is not enough to represent the complicated face images with large variations of poses and/or illuminations. To overcome this weakness, we propose a second-order mixture-of-eigenfaces method that combines the second-order eigenface method (ISO MPG m5750, Noordwijkerhout, March 2000) and the mixture-of-eigenfaces method (a.k.a. Gaussian mixture model (Proceedings IJCNN2001, 2001). In this method, we use a couple of mixtures of multiple eigenface sets: one is a mixture of multiple approximate eigenface sets for face images and another is a mixture of multiple residual eigenface sets for residual face images. Each mixture of multiple eigenface sets has been obtained from expectation maximization learning consecutively. Based on two mixture of multiple eigenface sets, each face image is represented by a couple of feature vectors obtained by projecting the face image onto a selected approximate eigenface set and then by projecting the residual face image onto a selected residual eigenface set. Recognition is performed by the distance in the feature space between the input image and the template image stored in the face database. Simulation results show that the proposed second-order mixture-of-eigenfaces method is best for face images with illumination variations and the mixture-of-eigenfaces method is best for the face images with pose variations in terms of average of the normalized modified retrieval rank and false identification rate.

Original languageEnglish
Pages (from-to)337-349
Number of pages13
JournalPattern Recognition
Issue number2
Publication statusPublished - 2004 Feb

Bibliographical note

Funding Information:
The authors would like to thank the Ministry of Education of Korea for its financial support toward the Electrical and Computer Engineering Division at POSTECH through its BK21 program. This research was also partially supported by a grant (R01-1999-000-00224-0) from Korea Science & Engineering Foundation and a grant from Korea Telecom, Korea.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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