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020			_aTHE0009629 (Local) _qHardback
040			_aUMP _beng _cUMP _erda
090			_aFKOM .A36 2022 r Thesis
100	0		_aMohammed Ahmed Talab, _eauthor.
245	1	0	_aIntegration of blcm and flbp in low resolution face recognition / _cMohammed Ahmed Talab
264		1	_aKuantan, Pahang : _bUMP, _c2022
264		4	_c© 2022
300			_axiv, 122 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
337			_2rdamedia _acomputer
338			_2rdacarrier _avolume
338			_2rdacarrier _acomputer disc
347			_2rda _atext file _bPDF
500			_aFaculty of Computing
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2022
504			_aIncludes bibliographical references
520	3		_aFace recognition from face image has been a fast-growing topic in biometrics research community and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years. These techniques work well on grayscale and colour images with very few techniques deal with binary and low resolution image. With binary image becoming the preferred format for low face resolution analysis, there is need for further studies to provide a complete solution for image-based face recognition system with higher accuracy. To overcome the limitation of the existing techniques in extracting distinctive features in low resolution images due to the contrast between the face and background, we proposed a statistical feature analysis technique to fill in the gaps. To achieve this, the proposed technique integrates Binary Level Occurrence Matrix (BLCM) and Fuzzy Local Binary Pattern (FLBP) named BLCM-FLBP to extract global and local features of face from face low resolution images. The purpose of BLCM-FLBP is to distinctively improve performance of edge sharpness between black and white pixels in the binary image and to extract significant data relating to the features of face pattern. Experimental results on Yale and FEI datasets validates the superiority of the proposed technique over the other top-performing feature analysis techniques methods by utilizing different classifier which is Neural network (NN) and Random Forest (RF). The proposed technique achieved performance accuracy of 93.16% (RF), 95.27% (NN) when FEI dataset used, and the accuracy of 94.54% (RF), 93.61% (NN) when Yale.B used. Hence, the proposed technique outperforming other technique such as Gray Level Co-Occurrence Matrix (GLCM), Bag of Word (BOW), Fuzzy Local Binary Pattern (FLBP) respectively and Binary Level Occurrence Matrix (BLCM).
610	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses
942			_2lcc _cTHESIS