Authors:
Amer G. Binsaadoon
and
El-Sayed M. El-Alfy
Affiliation:
King Fahd University of Petroleum and Minerals, Saudi Arabia
Keyword(s):
Biometric, Human Identification, Gait Recognition, Local Binary Pattern, Fuzzy Logic.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
With the increasing security breaches nowadays, automated gait recognition has recently received increasing importance in video surveillance technology. In this paper, we propose a method for human identification at distance based on Fuzzy Local Binary Pattern (FLBP). After the Gait Energy Image (GEI) is generated as a spatiotemporal summary of a gait video sequence, a multi-region partitioning is applied and FLBP based features are extracted for each region. We also evaluate the performance under the variation of some factors including viewing angle, clothing and carrying conditions. The experimental work showed that GEI-FLBP with partitioning has remarkably enhanced the identification accuracy.