Abstract
In this paper we give a comparative analysis of performance of feed forward neural network and generalized regression neural network based face recognition. We use different inner epoch for different input pattern according to their difficulty of recognition. We run our system for different number of training patterns and test the system’s performance in terms of recognition rate and training time. We run our algorithm for face recognition application using Principal Component Analysis and both neural network. PCA is used for feature extraction and the neural network is used as a classifier to identify the faces. We use the ORL database for all the experiments.
Highlights
The task of recognition of human faces is quite complex
We study the results from these neural networks based face recognition systems to find which neural network gives better results in all circumstances such as changing of lighting condition, expression rotation of human faces and distractions like glasses, beards, and moustaches
From the experiment we conclude that the feedforward neural network has recognition rate 96% which is more in comparison to generalized regression neural network
Summary
The human face is full of information but working with all the information associated with the face is time consuming and less efficient. For example on Air ports, Military bases, Government offices etc These systems can help in places where unauthorized access of persons is prohibited. A face recognition system can be considered as a good system if it can fetch the important features, without making the system complex and can make use of those features for recognizing the unseen faces. Once a set of eigenfaces is computed, a face image can be approximately reconstructed using a weighted combination ofthe eigen-faces. The projection weights form a feat ure vector for face representation and recognition. When a new test image is given, the weights are computed by projecting the image onto the eigen-face vectors. Suppose there are P patterns and each pattern has t training images of m x n configuration
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