Face identification using back-propagation adaptive multiwavenet

Abstract EN

Face Identification is an important research topic in the field of computer vision and pattern recognition and has become a very active research area in recent decades.

Recently multiwavelet-based neural networks (multi wave nets) have been used for function approximation and recognition, but to our best knowledge it has not been used for face Identification.

This paper presents a novel approach for the Identification of human faces using Back-Propagation Adaptive Multi wave net.

The proposed multi wave net has a structure similar to a multilayer perceptron (MLP) neural network with three layers, but the activation function of hidden layer is replaced with multi scaling functions.

In experiments performed on the ORL face database it achieved a recognition rate of 97.75 % in the presence of facial expression, lighting and pose variations.

Results are compared with its wavelet-based counterpart where it obtained a recognition rate of 10.4 %.

The proposed multi wave net demonstrated very good recognition rate in the presence of variations in facial expression, lighting and pose and outperformed its wavelet-based counterpart.