An improved ABC algorithm approach using SURF for face identification

Authors:
Chidambaram Chidambaram;Marlon Subtil Marçal;Leyza Baldo Dorini;Hugo Vieira Neto;Heitor Silvério Lopes
Affiliations:
State University of Santa Catarina-UDESC, Brazil,Federal University of Technology - Paraná - UTFPR, Brazil;Federal University of Technology - Paraná - UTFPR, Brazil;Federal University of Technology - Paraná - UTFPR, Brazil;Federal University of Technology - Paraná - UTFPR, Brazil;Federal University of Technology - Paraná - UTFPR, Brazil
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

Citing 4
Cited 0

Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Synthesis of interest point detectors through genetic programming

Proceedings of the 8th annual conference on Genetic and evolutionary computation
2D and 3D face recognition: A survey

Pattern Recognition Letters
Speeded-Up Robust Features (SURF)

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

Face recognition is being intensively studied in the areas of computer vision and pattern recognition. Working on still images with multiple faces is a challenging task due to the inherent characteristics of the images, the presence of blur, noise and occlusion, as well as variations of illumination, pose, rotation and scale. Besides being invariant to these factors, face recognition systems must be computationally efficient and robust. Swarm intelligence algorithms can be used for object recognition tasks. Based on this context, we propose a new approach using an improved ABC implementation and the interest point detector and descriptor SURF. To assess the robustness of our approach, we carry out experiments on images of several classes subject to different acquisition conditions.