Face Matching in Large Database by Self-Organizing Maps

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.

Face image retrieval by using Haar features

“…Navarrete and Ruiz-Del-Solar [8] used treestructured self-organizing map (TS-SOM) to organize face images for fast retrieval. Chow and Rahman [5] combined multiple facial features and multi-SaM for face matching. The SaM-based methods usually do not consider the face variations of the same person, but this characteristic is important in face retrieval.…”