Gender recognition using PCA and DCT of face images

Authors:
Ondrej Smirg;Jan Mikulka;Marcos Faundez-Zanuy;Marco Grassi;Jiri Mekyska
Affiliations:
Brno University of Technology, UTKO, Purkynova, Czech Republic;Brno University of Technology, UTKO, Purkynova, Czech Republic;Escola Universitària Politècnica de Mataró, Mataró, Spain;Department of Biomedical, Electronic and Telecommunication Engineering, Politecnica delle, Marche, Ancona, Italy;Brno University of Technology, UTKO, Purkynova, Czech Republic
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Year:
2011

Citing 7
Cited 0

Fundamentals of digital image processing

Fundamentals of digital image processing
SexNet: A neural network identifies sex from human faces

NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Geometric Primitive Extraction Using a Genetic Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient face verification method in a transformed domain

Pattern Recognition Letters
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Protecting DCT Templates for a Face Verification System by Means of Pseudo-random Permutations

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
2D-DCT Distance Based Face Recognition Using a Reduced Number of Coefficients

SIBGRAPI '09 Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we propose a gender recognition algorithm of face images. We have used PCA and DCT for dimensionality reduction. The algorithm is based on a genetic algorithm to improve the selection of training set of images for the PCA algorithm. Genetic algorithm helps to select the images, which best represent each gender, from the image database. We have evaluated a nearest neighbor classifier as well as a neural network. Experimental results show a correct identification rate of 85,9%.