000			01912nam a22002417a 4500
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005			20251218162718.0
008			251218b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aQA76.9 A43 A58 2025
082			_a.
100	1		_a Antipona, Clarence A.; Magsino III, Romeo R.
245			_aAn enhancement of haar cascade algorithm applied to face recognition for gatepass security
264		1	_a. _b. _cc2025
300			_bUndergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025
336			_2text _atext _btext
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: This study is focused on enhancing the Haar Cascade algorithm to decrease the false positive and false negative rate in face matching, improve face detection accuracy and detect real human faces even under challenging conditions. The face recognition library from OpenCV was implemented with Haar Cascade where 128-dimensional vectors representing the unique features of a face were encoded. A subprocess was applied where the grayscale image from the Haar Cascade was converted to RGB to improve the face encoding. Logical process and filtering were used to decrease non-face detection. The Enhanced Haar Cascade Algorithm produced a 98.39% accuracy rate, 63.59% precision rate, 98.30% recall rate, and 72.23% in F1 Score. The original and enhanced algorithms used the Confusion Matrix Test with 301,950 comparison using the same dataset of 550 images. The 98.39% accuracy rate shows a significant decrease in false positive and false negative rates in facial recognition. Face matching and face detection are more accurate in images with complex backgrounds, lighting variations, and occlusions, or even those with similar attributes.
526			_aF
655			_aacademic writing
942			_2lcc _cMS
999			_c37384 _d37384