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Face Matching in Large Database by Self-Organizing Maps

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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.

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Authors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong

    Tommy W. S. Chow & M. K. M. Rahman

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  1. Tommy W. S. Chow
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  2. M. K. M. Rahman
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Correspondence to Tommy W. S. Chow.

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Chow, T.W.S., Rahman, M.K.M. Face Matching in Large Database by Self-Organizing Maps. Neural Process Lett 23, 305–323 (2006). https://doi.org/10.1007/s11063-006-9004-y

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