Comparison of Different Algorithm for Face Recognition

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Hemant Makwana
Hemant Makwana
2
Taranpreet Singh
Taranpreet Singh
1 RGPV

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GJCST Volume 13 Issue F9

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Comparison of Different Algorithm for Face Recognition Banner
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This paper is about the different algorithms which are used for face recognition. There are so many algorithms which are available for face recognition .There are two approaches by which the face can be recognize i.e. face Geometry based and face appearance based. The appearance based technique is also sub divided into two technique i.e. local feature and global feature based. The technique of local feature based are Discrete Cosine Transform (DCT).In this paper we study the two global features (holistic) appearance based algorithm i.e. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) in which every face image is converted into 1D, we are using 1D for all the calculation and then compare these two algorithm with the help of FAR (False Acceptance Rate),FRR (False Rejection Rate),Time, Memory and checks which algorithm gives the better result.

12 Cites in Articles

References

  1. Mattew Turk,Alex Pentland Eigenfaces for Recognition.
  2. Sirovich Kirby (1990). Application of Karhunen-Loeve procedure for the characterization of human faces.
  3. M Turk,A Pentland (1991). Face recognition using Eigen faces.
  4. Kyungim Baek,Bruce Draper,J Beveridge,Kai She (2002). PCA vs. ICA: A Comparison on the FERET Data Set.
  5. T Chen,Wotao Yin,Xiang Sean Zhou,D Comaniciu,T Huang (2006). Total variation models for variable lighting face recognition.
  6. Jan Longin,Venugopal Latecki,Ari Rajagopal,Gross (2005). Image Retrieval and Reversible Illumination Normalization.
  7. P Hancock,V Bruce,A Burton Testing Principal Component Representations for Faces.
  8. Jonathon Shlens (1997). A Tutorial on Principal Component Analysis.
  9. Zhujie,Y Yu (1994). Face recognition with eigenfaces.
  10. S Debipers,A Broadhurst (1997). Face recognition using neural networks.
  11. Nazish (2001). Face recognition using neural networks.
  12. David Rumelhart,Geoffrey Hinton,Ronald Williams (1985). Learning Internal Representations by Error Propagation.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Hemant Makwana. 2014. \u201cComparison of Different Algorithm for Face Recognition\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 13 (GJCST Volume 13 Issue F9): .

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GJCST Volume 13 Issue F9
Pg. 17- 20
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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v1.2

Issue date

February 3, 2014

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English

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This paper is about the different algorithms which are used for face recognition. There are so many algorithms which are available for face recognition .There are two approaches by which the face can be recognize i.e. face Geometry based and face appearance based. The appearance based technique is also sub divided into two technique i.e. local feature and global feature based. The technique of local feature based are Discrete Cosine Transform (DCT).In this paper we study the two global features (holistic) appearance based algorithm i.e. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) in which every face image is converted into 1D, we are using 1D for all the calculation and then compare these two algorithm with the help of FAR (False Acceptance Rate),FRR (False Rejection Rate),Time, Memory and checks which algorithm gives the better result.

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Comparison of Different Algorithm for Face Recognition

Hemant Makwana
Hemant Makwana RGPV
Taranpreet Singh
Taranpreet Singh

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