Optimization technique to linear discriminant regression for face recognition

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    To improve the robustness of the linear regression model number of improvements have been made working on the different databases, the main aim of this paper is to show how an optimization algorithms improves the efficiency of linear discriminant regression methods and performance is evaluated, The features are extracted using Active Appearance Model then the classification is done via linear collaborative Discriminant regression classification (LCDRC) model Proposed by Xiaochao Qu. In the LCDRC classifier, the most important evaluation is projection matrix that might get multiplied to the features while classification. In order to select the optimal projection matrix, this paper proposes a improved whale optimization technique, which is the Enhanced form of Whale Optimization Algorithm (WOA). The proposed face recognition model compares its performance over other  conventional methods by varying the regularization constant value from0.5 to 2.5 and performance is taken in terms of measures like Accuracy, Specificity, Sensitivity, Precision, Negative Predictive Value (NPV), F1Score and Matthews Correlation Coefficient (MCC), False positive rate (FPR), False negative rate (FNR) and False Discovery Rate (FDR),and the efficiency by varying the regularization constant and the effectiveness of this model is proven.

     

     



  • Keywords


    Face Recognition; Active Appearance Model; Linear Collaborative Discriminant Regression Classification; Whale Optimization.

  • References


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Article ID: 20260
 
DOI: 10.14419/ijet.v7i4.20260




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