Authors: Vijayalakshmi G.V. Mahesh; Alex Noel Joseph Raj
Addresses: School of Electronics Engineering, VIT University, Vellore, India ' Embedded Systems Division, School of Electronics Engineering, VIT University, Vellore, India
Abstract: The paper proposes a face recognition system using Zernike moments (ZM) and feed forward neural network as a classifier. Magnitudes of the ZM, which are invariant to rotation, are used as feature vectors for efficient representation of the images. The experiment was conducted on the ORL and Texas 3D Face Recognition Database which has both colour and range images. The recognition performance with measures like overall recognition accuracy, false acceptance rate, false rejection rate and true rejection rate was evaluated with multilayer perceptron neural network, radial basis function neural network and probabilistic neural network for variable lengths of the feature vector using confusion matrix. The simulation results indicates that the invariant ZM with neural network classifier was successful in recognising the images constrained to different variations and illumination conditions. The overall classification accuracy of 99.7% with MLPNN and 99.6% with MLPNN was achieved with range images and grey images from Texas 3D Face Recognition Database, respectively. Furthermore, 99.5% accuracy with RBFNN was achieved from ORL database.
Keywords: invariant Zernike moments; multilayer perceptron neural networks; MLPNN; radial basis function neural networks; RBFNN; probabilistic neural networks; PNN; face recognition; confusion matrix; classification accuracy; false acceptance rate; FAR; false rejection rate; FRR; true rejection rate; TRR; biometrics; simulation.
International Journal of Biometrics, 2015 Vol.7 No.3, pp.286 - 307
Received: 01 May 2015
Accepted: 06 Aug 2015
Published online: 24 Sep 2015 *