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Face Detection using Principal Component Analysis (PCA)

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 95 - Number 17
Year of Publication: 2014
Authors:
Pushpak Dave
Jatin Agarwal
Tarun Metta
10.5120/16690-6815

Pushpak Dave, Jatin Agarwal and Tarun Metta. Article: Face Detection using Principal Component Analysis (PCA). International Journal of Computer Applications 95(17):37-40, June 2014. Full text available. BibTeX

@article{key:article,
	author = {Pushpak Dave and Jatin Agarwal and Tarun Metta},
	title = {Article: Face Detection using Principal Component Analysis (PCA)},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {95},
	number = {17},
	pages = {37-40},
	month = {June},
	note = {Full text available}
}

Abstract

Face Detection makes it possible to use the facial images of a person to authenticate him into secure system, for criminal identification, for passport verification etc. It is done by Principal Component Analysis (PCA). Face images are projected onto a face space that encodes best variation among known face images. The face space is collection of Eigen face. In the algorithm, initially video segmented using shot boundary detection techniques. Specifically, it can detect both the cut and gradual shot transitions in video. For detecting the shot boundary haar wavelet transform is used. In this method, each frame and its haar wavelet transform image is correlated for detection the shot. By setting the threshold of frame correlation shot boundaries can be detected. Video segmentation can be used in various application like video summarization, video search, and video annotation.

References

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