Gaussian Mixture Model Coupled with Independent Component Analysis for Palmprint Verification

In this paper we present a new scheme for Palmprint verification. The proposed method can be viewed as a combination of Gaussian Mixture Model (GMM) followed by Independent Component Analysis (ICA I and ICA II) applied directly on the pixels. This approach follows the path opened by previous works making use of GMM followed by Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) projection methods for face recognition and is expected to be efficient to tackle major variations in the data. Extensive experiments have been carried out on PolyU palmprint database. We show that ICA I performs better than PCA, LDA and ICA II in the non Mixture Model case, while in the Mixture Model case, ICA II MM outperforms the three other Mixture Models (PCA MM, LDA MM, ICA I MM). Moreover, using artificially corrupted data (noisy palmprints), we show the robustness of Mixture Model approaches to noise, a property which can be very interesting in realistic situations.