Robust Face Recognition and Retrieval Using Neural-Network-Based Quantization of Gabor Jets and Statistical Graph Matching

This paper proposes a novel distributional-based approach towards the face retrieval problem. Face features are initially extracted in the frequency domain via Gabor filtering, producing a large number of Gabor jets. The Neural-Gas vector quantizer is used to extract representative samples of the multivariable face distribution. In this way, only a small amount of Gabor jet signatures is utilized. Each face image is then represented as a distribution of a few signatures in the frequency space, containing all the important information. The similarity between two images is finally assessed by comparing the corresponding distributions directly in the frequency space using the multivariate Waid-Wolfowitz test (WW-test), a non- parametric statistical test dealing with the multivariate "Two-Sample Problem". Experimental results drawn from a standard collection of face-images show a significantly improved performance relative to other typical methods.