Comparative Assessment of Performance of LDA, LR and SVM in the Application of Face Detection

Authors

  • S Paul Dept. of Computer Science & Engineering, JIS College of Engg., Kalyani, India
  • P Rakshit Dept. of Computer Science & Engineering, JIS College of Engg., Kalyani, India
  • J. Mistri Dept. of Computer Science & Engineering, JIS College of Engg., Kalyani, India
  • I Nath Dept. of Computer Science & Engineering, JIS College of Engg., Kalyani, India
  • S Biswas Dept. of Computer Science & Engineering, JIS College of Engg., Kalyani, India
  • D Singh Dept. of Computer Science & Engineering, JIS College of Engg., Kalyani, India
  • R Sen Dept. of Computer Science & Engineering, JIS College of Engg., Kalyani, India

Keywords:

Face detection, Face Recognition, PCA, SVM, LDA, and LR

Abstract

In biometrics research face detection and recognition is a very popular topic and it has distinct advantages because of its non-contact process. This type of technology extensively draws attention due to its huge application and market value. like video surveillance system for detecting suspicious object. Face based recognition system is more popular over other biometrics because of its uniqueness. Face recognition is very difficult task because human face is a dynamic object and has variability in its appearance. So, here accuracy and speed of recognition is Min issue. The purpose of the paper is correctly recognized a person from an image face or a video. To correctly identify a person we have used three techniques: Linear discriminant analysis (LDA), Logistic Regression (LR) and support vector Machine (SVM) techniques with Principle Components Analysis (PCA) which extract the features and reduce dimensionality. The LDA and LR technique produce more accurate result compare to other methods. This paper achieved 93% successful recognition rate for recognizing different face database.

References

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Published

2020-02-28

How to Cite

[1]
S. Paul, “Comparative Assessment of Performance of LDA, LR and SVM in the Application of Face Detection”, Int. J. Comp. Sci. Eng., vol. 8, no. 1, pp. 44–48, Feb. 2020.

Issue

Vol. 8 No. 1 (2020): IJCSE Special Issue Feb Edition

Section

Research Article

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