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Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Akram Alsubari, R. J. Ramteke

Akram Alsubari and R J Ramteke. Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments. International Journal of Computer Applications 172(5):31-34, August 2017. BibTeX

	author = {Akram Alsubari and R. J. Ramteke},
	title = {Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {172},
	number = {5},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {31-34},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017915162},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This paper describes the recognition of multimodal biometric systems based on the face and palmprint features. The common feature extraction technique between the palmprint and face recognition system is the Local Binary Pattern. In the palmprint recognition, the region of palm was extracted form the entire hand. The Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) were used to extract the features of palm. The Zernike moments and LBP were used to extract the features of face. The features of palm and face were integrated as feature vector. To evaluated the accuracy of the system, different classifications were applied such as SVM, KNN, Linear Discriminant. This experiment was performed on the ORL and CASIA database. The proposed system was tested on the said database and found to be satisfactory.


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LBP, HOG, Zernike Moments, ROI, SVM, KNN.