Confidence Based Gating of Colour Features for Face Authentication

We address the problem of fusing colour information for face authentication. The performance of a face verification system in different colour spaces is experimentally studied first. The verification process is based on the normalised correlation measure within the LDA feature space. The confidence level of the measurement made is then calculated for each colour subspace. Confidence measures are used within the framework of a gating process in order to select a subset of colour space classifiers. The selected classifiers are finally combined using the voting rule for decision making. Using the proposed method, the performance of the verification system is considerably improved as compared to the intensity space. The proposed colour fusion scheme also outperforms the best colour space in different conditions.