Component-based LDA face description for image retrieval and MPEG-7 standardisation

  • Authors:

  • Tae-Kyun Kim;Hyunwoo Kim;Wonjun Hwang;Josef Kittler

  • Affiliations:

  • Computing Lab., Samsung Advanced Institute of Technology, San 14-1, Nongseo-ri, Kiheung-eup, Yongin, Kyungki-do 449-712, South Korea;Computing Lab., Samsung Advanced Institute of Technology, San 14-1, Nongseo-ri, Kiheung-eup, Yongin, Kyungki-do 449-712, South Korea;Computing Lab., Samsung Advanced Institute of Technology, San 14-1, Nongseo-ri, Kiheung-eup, Yongin, Kyungki-do 449-712, South Korea;Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 5XH, UK

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2005

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Abstract

We propose a method of face description for facial image retrieval from a large data base and for MPEG-7 (Moving Picture Experts Group) standardisation. The novel descriptor is obtained by decomposing a face image into several components and then combining the component features. The decomposition combined with LDA (Linear Discriminant Analysis) provides discriminative facial descriptions that are less sensitive to light and pose changes. Each facial component is represented in its Fisher space and another LDA is then applied to compactly combine the features of the components. To enhance retrieval accuracy further, a simple pose classification and transformation technique is performed, followed by recursive matching. Our algorithm has been developed to deal with the problem of face image retrieval from huge databases such as those found in Internet environments. Such retrieval requires a compact face representation which has robust recognition performance under lighting and pose variations. The partitioning of a face image into components offers a number of benefits that facilitate the development of an efficient and robust face retrieval algorithm. Variation in image statistics due to pose and/or illumination changes within each component region can be simplified and more easily captured by a linear encoding than that of the whole image. So an LDA encoding at the component level facilitates better classification. Furthermore, a facial component can be weighted according to its importance. The component with a large variation is weighted less in the matching stage to yield a more reliable decision. The experimental results obtained on the MPEG-7 data set show an impressive accuracy of our algorithm as compared with other methods including conventional PCA (Principal Component Analysis)/ICA (Independent Component Analysis)/LDA methods and the previous MPEG-7 proposals.