Face matching in large database by self-organizing maps
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 305-323 |
Journal / Publication | Neural Processing Letters |
Volume | 23 |
Issue number | 3 |
Publication status | Published - Jun 2006 |
Link(s)
Abstract
A novel self-organizing map (SOM) based retrieval system is proposed for performing face matching in large database. The proposed system provides a small subset of faces that are most similar to a given query face, from which user can easily verify the matched images. The architecture of the proposed system consists of two major parts. First, the system provides a generalized integration of multiple feature-sets using multiple self-organizing maps. Multiple feature-sets are obtained from different feature extraction methods like Gabor filter, Local Autocorrelation Coefficients, etc. In this platform, multiple facial features are integrated to form a compressed feature vector without concerning scaling and length of individual feature set. Second, an SOM is trained to organize all the face images in a database through using the compressed feature vector. Using the organized map, similar faces to a query can be efficiently identified. Furthermore, the system includes a relevance feedback to enhance the face retrieval performance. The proposed method is computationally efficient. Comparative results show that the proposed approach is promising for identifying face in a given large image database. © Springer 2006.
Research Area(s)
- Face matching, Feature integration, Relevance feedback, Self-organizing map
Citation Format(s)
Face matching in large database by self-organizing maps. / Chow, Tommy W. S.; Rahman, M. K M.
In: Neural Processing Letters, Vol. 23, No. 3, 06.2006, p. 305-323.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review