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This research proposed a new approach to recognize face image under various lighting expression. The Proposed method is started by generating of the Left Diagonal Matrix (LDM) and the Right Diagonal Matrix (RDM). Subsequently, the dimensionality reduction is conducted by using Two- Dimensional Eigenface of the LDM and the RDM. The results of dimensionality reduction are selected the maximum value based on the corresponding features. Finally, the geometric similarity measurement model is carried out to obtain the recognition rate. The proposed method was evaluated using three different facial image databases, which are the YALE, the ORL and the UoB face image databases. Two until six column features were used to measure the similarity between the training and the testing sets. Experimental results revealed that the proposed method produced 94.7%, 94%, and 92.2% recognition rate for the YALE, the ORL, and the UoB face image databases respectively. The proposed method was also compared to the other methods. On the ORL face image database, the comparison results show that the proposed method outperformed to the Eigenface, the Fisherface, and the Laplacianfaces but not for O-Laplacianfaces and Two-Dimensional Principal Component Analysis method. On the YALE and the UoB face image databases, the proposed method outperformed to the other appearance methods which are the Eigenface, the Fisherface, the Laplacianfaces, the O-Laplacianfaces and Two- Dimensional Principal Component Analysis methods.

Maximum Value of the Symmetrical Diagonal Matrix Based Two - Dimensional Eigenface s for Facial Recognition on the Variant Lighting and Expressions