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HFDA: Hybrid Face Detection Algorithm for Analyzing of Biometric Application

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Surbhi Choudhary, Ashish Tiwari

Surbhi Choudhary and Ashish Tiwari. HFDA: Hybrid Face Detection Algorithm for Analyzing of Biometric Application. International Journal of Computer Applications 157(4):10-14, January 2017. BibTeX

	author = {Surbhi Choudhary and Ashish Tiwari},
	title = {HFDA: Hybrid Face Detection Algorithm for Analyzing of Biometric Application},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {4},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {10-14},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017912659},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. In this age of era, different authentication and authorization is required for security perspective. So in this concern for verifying or identify a person in the digital image, features extracted from the digital image are compared with features of the images in the facial database. In this paper, we have been implemented HFDA i.e. Hybrid Face Detection Algorithm using canny filter and LDA approach that enhance figures are found to be better than some of the work reported in literature.


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Face Recognition, Feature selection, LDA, PCA, Face Data, Computer Vision, Authentication, Canny Edge Filter.