Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images
This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the h...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taiwan Association of Engineering and Technology Innovation
2021-01-01
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| Series: | International Journal of Engineering and Technology Innovation |
| Subjects: | |
| Online Access: | https://ojs.imeti.org/index.php/IJETI/article/view/6174 |
| _version_ | 1797808601249611776 |
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| author | R. Rizal Isnanto Adian Rochim Dania Eridani Guntur Cahyono |
| author_facet | R. Rizal Isnanto Adian Rochim Dania Eridani Guntur Cahyono |
| author_sort | R. Rizal Isnanto |
| collection | DOAJ |
| description |
This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.
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| first_indexed | 2024-03-13T06:40:04Z |
| format | Article |
| id | doaj.art-479a808b30e84672915428b7ba6ce383 |
| institution | Directory Open Access Journal |
| issn | 2223-5329 2226-809X |
| language | English |
| last_indexed | 2024-03-13T06:40:04Z |
| publishDate | 2021-01-01 |
| publisher | Taiwan Association of Engineering and Technology Innovation |
| record_format | Article |
| series | International Journal of Engineering and Technology Innovation |
| spelling | doaj.art-479a808b30e84672915428b7ba6ce3832023-06-08T18:19:16ZengTaiwan Association of Engineering and Technology InnovationInternational Journal of Engineering and Technology Innovation2223-53292226-809X2021-01-0111110.46604/ijeti.2021.6174Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution ImagesR. Rizal Isnanto0Adian Rochim1Dania Eridani2Guntur Cahyono3Department of Computer Engineering, Diponegoro University, Semarang, IndonesiaDepartment of Computer Engineering, Diponegoro University, Semarang, IndonesiaDepartment of Computer Engineering, Diponegoro University, Semarang, IndonesiaDepartment of Computer Engineering, Diponegoro University, Semarang, Indonesia This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%. https://ojs.imeti.org/index.php/IJETI/article/view/6174face recognitionlinear binary pattern histogramlow resolutionhistogram equalization |
| spellingShingle | R. Rizal Isnanto Adian Rochim Dania Eridani Guntur Cahyono Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images International Journal of Engineering and Technology Innovation face recognition linear binary pattern histogram low resolution histogram equalization |
| title | Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images |
| title_full | Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images |
| title_fullStr | Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images |
| title_full_unstemmed | Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images |
| title_short | Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images |
| title_sort | multi object face recognition using local binary pattern histogram and haar cascade classifier on low resolution images |
| topic | face recognition linear binary pattern histogram low resolution histogram equalization |
| url | https://ojs.imeti.org/index.php/IJETI/article/view/6174 |
| work_keys_str_mv | AT rrizalisnanto multiobjectfacerecognitionusinglocalbinarypatternhistogramandhaarcascadeclassifieronlowresolutionimages AT adianrochim multiobjectfacerecognitionusinglocalbinarypatternhistogramandhaarcascadeclassifieronlowresolutionimages AT daniaeridani multiobjectfacerecognitionusinglocalbinarypatternhistogramandhaarcascadeclassifieronlowresolutionimages AT gunturcahyono multiobjectfacerecognitionusinglocalbinarypatternhistogramandhaarcascadeclassifieronlowresolutionimages |