Comparison on PCA ICA and LDA in Face Recognition

Christry, C.; Pavithra, J.; Sathya, F.Mahimai

doi:10.20894/ijdmta.102.003.001.010

Cited by 1 publication

(1 citation statement)

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“…ICA minimize both higher-order and second-order dependencies in the input data, and attempts to determine the basics together with statistically independent data. ICA is a statistical method of transforming observed multidimensional random vectors into component vector that are statistically independent [16]. However, PCA which relies on the pair wise relationship between pixels in the image database, can only represent second-order inter-pixel relationships or relationships that capture the amplitude spectrum of an image, not the phase spectrum of the image.…”

Section: Independnet Component Analysis (Ica)mentioning

confidence: 99%

Face Recognition: A Literature Review

Barnouti¹,

Al-Dabbagh²,

Matti³

2016

IJAIS

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Face recognition have gained a great deal of popularity because of the wide range of applications such as in entertainment, smart cards, information security, law enforcement, and surveillance. It is a relevant subject in pattern recognition, computer vision, and image processing. Two major methods are used for features extraction, which can be classified into appearance-based and Model-based methods.Appearance-based methods use global representations to identify a face. Model-based face methods aim to construct a model of the human face that capture facial variations. Image similarity is the distance between the vectors of two images. This paper contains Four sections. The first section discusses face recognition applications with examples. The second section discuss the common feature face recognition methods. The third section discuss distance measurement classifiers. The fourth section discuss different face recognition databases.

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Section: Independnet Component Analysis (Ica)mentioning

confidence: 99%