Application of Hybrid Algorithm to Real-Time Face Recognition

2013 ◽  
Vol 278-280 ◽  
pp. 1309-1313 ◽  
Author(s):  
Chi Jo Wang ◽  
Juing Shian Chiou ◽  
Yu Chia Hu
Keyword(s):  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Real Time ◽  
Classification Accuracy ◽  
Hybrid Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Recognition Systems

This paper proposed the principal component analysis (PCA) and support vector machine-genetic algorithm (SVM-GA) to the real-time face recognition. The integrated scheme aims to apply the SVM-GA method to improve the validity of PCA based real-time recognition systems. Experimental results show that the proposed method simplifies features effectively and obtains a higher classification accuracy.

Implementation of Hybrid Algorithms for Real-Time Face Recognition

2013 ◽  
Vol 311 ◽  
pp. 179-184
Author(s):  
Ming Yuan Shieh ◽  
Yu Chia Hu ◽  
Juing Shian Chiou
Keyword(s):  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Real Time ◽  
Classification Accuracy ◽  
Principal Component ◽  
Hybrid Algorithms ◽  
Support Vector ◽  
Hybrid Scheme ◽  
Recognition Systems

This paper proposed hybrid algorithms of principal component analysis (PCA) and support vector machine-genetic algorithm (SVM-GA) for real-time face recognition. The hybrid scheme aims to apply the SVM-GA to improve the validity of PCA based real-time recognition systems. Experimental results demonstrate the proposed method simplifies features effectively and obtains a higher classification accuracy.

scholarly journals Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ming-Yuan Shieh ◽  
Juing-Shian Chiou ◽  
Yu-Chia Hu ◽  
Kuo-Yang Wang
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Real Time ◽  
Selection Process ◽  
Principal Component ◽  
Facial Feature ◽  
Human Robot Interaction ◽  
Support Vector ◽  
Classification Problems ◽  
Recognition Systems

This paper incorporates principal component analysis (PCA) with support vector machine-particle swarm optimization (SVM-PSO) for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

Analysis of age invariant face recognition using quadratic support vector machine-principal component analysis

2021 ◽  
pp. 1-15
Author(s):  
Ashutosh Dhamija ◽  
R. B. Dubey
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Identity Recognition ◽  
The Face ◽  
A New Technique ◽  
Age Range

Face recognition is one of the most challenging and demanding field, since aging affects the shape and structure of the face. Age invariant face recognition is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The Age invariant face recognition, however, is still evolving and evolving, providing substantial potential for further study and progress inaccuracy. Major issues with the age invariant face recognition involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the age invariant face recognition systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVM-PCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging dataset of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%.

scholarly journals Performance of Principal Component Analysis and Orthogonal Least Square on Optimized Feature Set in Classifying Asphyxiated Infant Cry Using Support Vector Machine

2018 ◽  
Vol 9 (1) ◽  
pp. 139
Author(s):  
R. Sahak ◽  
W. Mansor ◽  
Khuan Y. Lee ◽  
A. Zabidi
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Least Square ◽  
Support Vector ◽  
Infant Cry ◽  
Orthogonal Least Square

<p>An investigation into optimized support vector machine (SVM) integrated with principal component analysis (PCA) and orthogonal least square (OLS) in classifying asphyxiated infant cry was performed in this study. Three approaches were used in the classification; SVM, PCA-SVM, and OLS-SVM. Various numbers of features extracted from Mel-frequency Cepstral coefficient (MFCC) were tested to obtain the optimal parameters of SVM kernels. Once the optimal feature set is obtained, PCA and OLS selected the most significant features and the optimized SVM then classified the selected cry patterns. In PCA-SVM, eigenvalue-one-criterion (EOC), cumulative percentage variance (CPV) and the Scree test (SCREE) were used to select the most significant features. SVM with radial basis function (RBF) kernel was chosen in the classification stage. The classification accuracy and computation time were computed to evaluate the performance of each method. The best method for classifying asphyxiated infant cry is PCA-SVM with EOC since it produces the highest classification accuracy which is 94.84%. Using PCA-SVM, the classification process was performed in 1.98s only. The results also show that employing feature selection techniques could enhance the classifier performance.</p>

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