A REVIEW PAPER ON ON THE DESIGN OPTIMIZATION FOR ENHANCED FACE RECOGNITION
Journal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.4, No. 12)Publication Date: 2015-12-30
Authors : R.C. Gangwar;
Page : 557-562
Keywords : Face Detection; Face recognition; pattern recognition; PSO; PCA;
- OPTIMIZED MODELIN G AND DESIGN OF STEEL FRAMES IN DIFFERENT SEISMIC ZONES USING ETABS SOFTWARE
- Analysis and Optimum Design for Steel Moment Resisting Frames to Seismic Excitation using combination of ELF and NTH methods
- Analysis and Optimum Design for Steel Moment Resisting Frames to Seismic Excitation using combination of ELF and NTH methods
- Comparative Study of Analysis of G 6 Building for Different Seismic Zones using STAAD.PRO and ETABS A Review
- Wind and Seismic Analysis of High Rise Building With and Without Steel Bracing Using ETABS
Abstract
Face Recognition is one of the problems which can be handled very well using Hybrid techniques or mixed transform rather than single technique. This paper deals with using of Particle Swarm Optimization techniques for Face Recognition. Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognit ion accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on PCA [1] [2] Subspace using Accelerated Binary Particle Swarm Optimization. ABPSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. This paper proposes a novel method of Binary Particle Swarm Optimization called Accelerated Binary Particle Swarm Optim ization (ABPSO) by intelligent acceleration of particles. Together with Image Pre - processing techniques such as Resolution Conversion, Histogram Equalization and Edge Detection, ABPSO is used for feature selection to obtain significantly reduced feature su bset and improved recognition rate. The performance of ABPSO is established by computing the recognition rate and the number of selected features on ORL database. For the implementation of this propose work we use the Image Processing Toolbox under Matlab software.
Other Latest Articles
- SIMULATION STUDIES ON SOLAR PHOTOVOLTAIC MODULE FOR DIFFERENT TEMPERATURE AND WAVE LENGTH
- EFFECTIVE RADAR TRACKING USING ADAPTIVE KALMAN FILTER
- A CROSSLAYER PROTOCOL DESIGN APPROACH BETWEEN PHY AND MAC LAYER FOR COGNITIVE RADIO NETWORKS
- REVIEW ON METHODS TO REDUCE DELAMINATION IN COMPOSITE LAMINATE
- INVESTIGATION MECHANISMS AND KINETICS OF OXIDATION IODIDE IONS IN HYDROTHERMAL WATERS VARIOUS OXIDANTS
Last modified: 2015-12-18 21:33:18