Face recognition is one of many important researches, and today, many
applications have implemented it. Through development of techniques like Principal Components Analysis (PCA), computers can now outperform human in many face recognition tasks, particularly those in which large database of faces must be searched.
Principal Components Analysis was used to reduce facial image dimension into fewer
variables, which are easier to observe and handle. Those variables then fed into artificial neural networks using backpropagation method to recognise the given facial image. The test results show that PCA can provide high face recognition accuracy. For the training faces, a correct identification of 100% could be obtained. From some of network combinations that have been tested, a best average correct identification of 91,11% could be obtained for the test faces while the worst average result is 46,67 % correct identification