Performance comparison of cascade and feed forward neural network for face recognition system

In this paper a neural network classifier is used for face recognition. The performance of a neural network to a large extent depends on its architecture. Two different architectures are investigated and presented in this paper. The cascade architecture (CASNN) and feed forward neural architecture (FFNN) are investigated. The feature extraction is performed using principal component analysis (PCA) as it reduces the computational burden. For a given database the features are extracted using PCA. The Olivetti Research Lab (ORL) database is used.The extracted features are divided into training set and testing set. The training data set is used to train both the neural network architectures. Both are tested extensively using testing data. A performance comparison is carried out and presented.