BFO-RLDA: A New Classification Scheme for Face Images Using Probabilistic Reasoning Model

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• 作者：Lingraj Dora (20)
  Sanjay Agrawal (21)
  Rutuparna Panda (21)
  
• 关键词：Face Classification ; Face Recognition ; Linear Discriminant Analysis (LDA) ; Regularized ; LDA (R ; LDA) ; Bacteria Foraging Optimization (BFO) ; Probabilistic Reasoning Model (PRM)
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：8297
• 期：1
• 页码：98-109
• 全文大小：376KB
• 作者单位：Lingraj Dora (20)
  Sanjay Agrawal (21)
  Rutuparna Panda (21)
  
  20. Electrical & Electronics Engineering Department, VSS University of Technology, Burla, Odisha, India
  21. Electronics & Telecommunication Engineering Department, VSS University of Technology, Burla, Odisha, India
  
• ISSN：1611-3349

文摘

Regularized-LDA (R-LDA) is a LDA-based method used for finding the nonzero eigenvalues and the corresponding eigenvectors. This paper presents a RLDA-based classification of face images which uses bacteria foraging optimization (BFO) algorithm for selecting the optimal discriminating features. The optimal features are then used by probabilistic reasoning model for classification of unknown face images. The ORL and the UMIST databases are used for experiment to demonstrate the performance of our proposed method. It is observed that our proposed method outperforms the existing method.