Identification of Best Suitable Samples for Training Database for Face Recognition using Principal Component Analysis with Eigenface Method | Zendy

Divyakant Meva | Zendy; C. K. Kumbharana | Zendy

Open Access

Identification of Best Suitable Samples for Training Database for Face Recognition using Principal Component Analysis with Eigenface Method

Author(s) -

Divyakant Meva,

C. K. Kumbharana

Publication year - 2015

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/20206-2463

Subject(s) - eigenface , computer science , biometrics , principal component analysis , authentication (law) , facial recognition system , face (sociological concept) , identification (biology) , principal (computer security) , component (thermodynamics) , pattern recognition (psychology) , artificial intelligence , data mining , computer security , social science , botany , physics , sociology , biology , thermodynamics

Security is one of the most important aspects in today’s computer environment. Especially, person authentication now a day is necessary to maintain security of computer based systems. Biometric authentication methods are becoming popular since last decade. Face recognition is one of the mature and popular biometric authentication methods. Today, with this paper, discussion on identifying best suitable samples for generating training database has been done. PCA based face recognition approach using Eigenface method has been discussed for the said purpose.

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