Recently classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. There are numbers of different Decision Support System (DSS) that has developed to operate on the minimum input data set or the output data set to give the correct decision. A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the face recognition performance. In this book, a face recognition system has been developed by applying multi-classifier fusion on the output of the three different classification methods namely Artificial Neural Network, Genetic Algorithm and Euclidean distance measure based on the Principal Component Analysis dimensionality reduction technique. Experimental results and performance analysis show the comparison results between multi-classifier fusion based face recognition system with individual classifier performance.