<![CDATA[Face recognition systems are very popular for authentication and security purposes. However, the development of recognition algorithms has been a true challenge, especially for handling the biometric characteristics of an object. One popular method for face representation is PCA (Principal Component Analysis). PCA is able to solve many recognition problems effectively and efficient by reducing the object (image) dimensions. However, PCA has its own drawbacks in the implementation. When the developed system uses a very large dimension of the input images, the system with the PCA method will have difficulties in constructing the covariance matrix and calculating the eigenvalues and eigenvectors. To overcome these problems, a face recognition system using the 2DPCA method is developed. The experiments show that the 2DPCA method could give higher recognition accuracies compared with that of the PCA method]]>
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