Particle swarm optimization based block feature selection in face recognition system

In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.

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Feature Selection using Particle Swarm Optimization for Thermal Face Recognition

Advances in Intelligent Systems and Computing - Applied Computation and Security Systems ◽

10.1007/978-81-322-1985-9_2 ◽

2014 ◽

pp. 25-35 ◽

Cited By ~ 5

Author(s):

Ayan Seal ◽

Suranjan Ganguly ◽

Debotosh Bhattacharjee ◽

Mita Nasipuri ◽

Consuelo Gonzalo-Martin

Keyword(s):

Feature Selection ◽

Particle Swarm Optimization ◽

Face Recognition ◽

Particle Swarm ◽

Swarm Optimization ◽

Thermal Face

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3D Angle Searching System with PSO for Face Recognition

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.2950 ◽

2013 ◽

Vol 284-287 ◽

pp. 2950-2954

Author(s):

Ching Tang Hsieh ◽

Chia Shing Hu ◽

Meng Shian Shih

Keyword(s):

Particle Swarm Optimization ◽

Face Recognition ◽

Open Source ◽

3D Modeling ◽

Recognition Rate ◽

Computation Time ◽

Recognition System ◽

Experimental Results ◽

Swarm Optimization ◽

Face Recognition System

Conventional 2D face recognition methods often struggle when a subject's head is turned even slightly to the side. In this study, a face recognition system based on 3D head modeling that is able to tolerate facial rotation angles was constructed by leveraging the Open source graphic library (OpenGL) framework. To minimize the extensive angle searching time that often occurs in conventional 3D modeling, Particle Swarm Optimization (PSO) was used to determine the correct facial angle in 3D. This reduced the angle computation time to 6 seconds, which is significantly faster than other methods. Experimental results showed that successful ID recognition can be achieved with a high recognition rate of 90%.

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APPLICATION OF QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION TO MOTOR IMAGERY EEG CLASSIFICATION

International Journal of Neural Systems ◽

10.1142/s0129065713500263 ◽

2013 ◽

Vol 23 (06) ◽

pp. 1350026 ◽

Cited By ~ 35

Author(s):

WEI-YEN HSU

Keyword(s):

Feature Selection ◽

Particle Swarm Optimization ◽

Motor Imagery ◽

Particle Swarm ◽

Recognition System ◽

Support Vector ◽

Data Sets ◽

Swarm Optimization ◽

Linear Discriminant ◽

Trial Analysis

In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain–computer interface (BCI) applications.

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