Face Identification based on Contourlet Transform and Multi-layer Perceptron Classifier {tag} {/tag} International Journal of Computer Applications Foundation of Computer Science (FCS), NY, USA Volume 133 - Number 2 Year of Publication: 2016 Authors: Ali Fattah Hassoon, Maher K. AL-Azzawi, Tariq Tashan 10.5120/ijca2016907384 {bibtex}2016907384.bib{/bibtex} Abstract Useful properties of the Contourlet Transform (CT) are exploited in this paper to investigate more discriminant features to enhance the face identification performance. In this paper a face identification system is suggested based on CT, and Multi-Layer Perceptron (MLP) Classifier. The main reasons behind using the CT are: First, the CT supports progressive data compression/expansion, hence it is used for image compression. Second, since the features in human face are not just horizontal or vertical. CT is utilized for feature extraction because it is a genuine 2-D transform that can capture the edge information in all directions. After decomposing an image by CT, low and high frequency coefficients of CT are calculated in different scales and various angles. The frequency coefficients are utilized as an input feature vector for a neural network classifier. Simple feed forward MLP neural network is used to achieve the identification process. The network parameters are tuned to optimal values, in order to produce fair comparison between different types of feature vectors. To evaluate the algorithm performance five different databases are used. Some of them of high variability, which examines the algorithm robustness against variability. In addition, the proposed algorithm is 1 / 3