Indian Journal of Science and Technology

Abstract

Objectives: This paper analyses the performance of single neuron cascade neural network with existing neural networks such as feed forward and radial basis function neural network for face recognition system. Methods: Face recognition system performance is based on the feature extraction and neural network architecture. Principal component analysis method is used for feature extraction and the extracted feature vectors are used to train the network. Using single neuron cascade architecture images are recognized. In the hidden layer single neuron is added one by one till the performance is achieved. Network is trained by set of train image and tested by a new test image. Recognition accuracy is calculated based on the recognized image. Findings: An effective classifier is identified for face recognition system. In this paper single neuron cascaded neural network is proposed for classification. In Feed forward Neural network the neurons in a layer get input from the previous layer and feed their output to the next layer. In Cascade neural network architecture the input to any layer includes all the outputs and the inputs from previous layers, which results in a cascaded interconnection between layers leading to more compact structures. Network design by cascading one neuron at a time until the desired performance is obtained can be automated. The proposed method gives systematic approach to design. It combines the advantages of both single layer feed forward neural network and multilayer feed forward neural network. Performance of the network is presented in terms of average recognition accuracy. Number of training image and test images are gradually increased from lower number samples per subject. If the number of training images is more recognition accuracy is improved. Proposed single neuron cascaded neural network out performs the existing network. Applications: This proposed method plays vital role in the field of pattern recognition, vision and human computer interactive based applications such as face recognition, surveillance, criminal identification and pass port verification. 
Keywords: Artificial Neural Network, Cascade Neural Network, Face Recognition, ORL database, Principal Component Analysis