000			02078nam a2200265 a 4500
001			vtls000055100
003			KUKTEM
005			20251114204505.0
008			110726t2009 my a f m 000 0 eng d
020			_aTHE0008085(Local)
039		9	_a201906191135 _bamy2 _c201906131012 _dhanafiah _c201107261607 _dida _y201107261606 _zida
040			_aUMP
090			_aTK7882.B56 F33 2009 rs Bc.
100	0		_aMuhammad Marzuq Mohd Sharip
245	1	0	_aFacial feature extraction / _cMuhammad Marzuq Mohd Sharip
246	3		_aFacial feature extraction _h[computer file]
260			_aKuantan, Pahang : _bUMP, _c2009
300			_axiv, 50 p. : _bill. (some col.) ; _c30 cm. + _e1 computer disc
502			_aProject paper (Bachelor of Electrical Engineering (Control and Instrumentation)) -- Universiti Malaysia Pahang - 2009
504			_aBibliography : p.[40]- 46
520	3		_aThis project presents the facial feature extraction system and face recognition system. The test image that used for this project contain various type. There are ten different images of each of 40 disntinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions; open or closed eyes, smiling or not smiling, and facial details; glasses or no glasses. All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position with tolerance for some side movement. For the facial feature extraction system, it is more focus on eye extraction. The eye will extracted from the face by finding the centroid of the eye region using threshold technique. For the recognition system, Principle Component Analysis (PCA) is used to match the test image with the database image. The system will find which database image has a maximum percentage based on similarity of the pattern of the image
650		0	_aBiometric identification
650		0	_aHuman face recognition (Computer science)
999			_aVIRTUA40 _c2934 _d2940
999			_aVTLSSORT0010*0030*0050*0080*0200*0390*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*9990*9992