Face detection is a common computer technology being used in human identification applications. It can also refer to the process of locating human faces in a visual scene. Face detection is a branched field of object detection where all objects in an image are detected including several classes like cars, trees, humans… etc. Also face detection problems branch into a lot of cases, some focus on frontal faces, others focus on side pose and so on. In this paper, a new face detection method based on Bilinear Interpolation image zooming method and image enhancement by Adaptive Histogram Equalization (AHE) method is proposed. The new method gives an encouraging results for crowded human images. By comparing the proposed method with the Viola-Jones algorithm, face detector using the cascade object detector, which supported in MATLAB, the new method gives excellent results in detecting human faces with different resolutions, poses and sizes. It succeeds in detecting most of the human faces in the tested images regardless of image sizes. The new method is tested on several images in Pratheepan dataset with crowded humans. Also, I tested the new method on many images collected from the Internet, whose can be classified as crowded human images. Experimental results show that the proposed Ad_L_Hist method is more efficient in detecting human faces in crowded human images.
| Published in | American Journal of Computer Science and Technology (Volume 3, Issue 4) |
| DOI | 10.11648/j.ajcst.20200304.11 |
| Page(s) | 68-75 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Face Detection, CIE_Lab Color Space, Bilinear Interpolation, Adaptive Histogram Equalization
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APA Style
Seham Elaw. (2020). Face Detection in Crowded Human Images by Bi-Linear Interpolation and Adaptive Histogram Equalization Enhancement. American Journal of Computer Science and Technology, 3(4), 68-75. https://doi.org/10.11648/j.ajcst.20200304.11
ACS Style
Seham Elaw. Face Detection in Crowded Human Images by Bi-Linear Interpolation and Adaptive Histogram Equalization Enhancement. Am. J. Comput. Sci. Technol. 2020, 3(4), 68-75. doi: 10.11648/j.ajcst.20200304.11
AMA Style
Seham Elaw. Face Detection in Crowded Human Images by Bi-Linear Interpolation and Adaptive Histogram Equalization Enhancement. Am J Comput Sci Technol. 2020;3(4):68-75. doi: 10.11648/j.ajcst.20200304.11
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Download@article{10.11648/j.ajcst.20200304.11,
author = {Seham Elaw},
title = {Face Detection in Crowded Human Images by Bi-Linear Interpolation and Adaptive Histogram Equalization Enhancement},
journal = {American Journal of Computer Science and Technology},
volume = {3},
number = {4},
pages = {68-75},
doi = {10.11648/j.ajcst.20200304.11},
url = {https://doi.org/10.11648/j.ajcst.20200304.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20200304.11},
abstract = {Face detection is a common computer technology being used in human identification applications. It can also refer to the process of locating human faces in a visual scene. Face detection is a branched field of object detection where all objects in an image are detected including several classes like cars, trees, humans… etc. Also face detection problems branch into a lot of cases, some focus on frontal faces, others focus on side pose and so on. In this paper, a new face detection method based on Bilinear Interpolation image zooming method and image enhancement by Adaptive Histogram Equalization (AHE) method is proposed. The new method gives an encouraging results for crowded human images. By comparing the proposed method with the Viola-Jones algorithm, face detector using the cascade object detector, which supported in MATLAB, the new method gives excellent results in detecting human faces with different resolutions, poses and sizes. It succeeds in detecting most of the human faces in the tested images regardless of image sizes. The new method is tested on several images in Pratheepan dataset with crowded humans. Also, I tested the new method on many images collected from the Internet, whose can be classified as crowded human images. Experimental results show that the proposed Ad_L_Hist method is more efficient in detecting human faces in crowded human images.},
year = {2020}
}
TY - JOUR T1 - Face Detection in Crowded Human Images by Bi-Linear Interpolation and Adaptive Histogram Equalization Enhancement AU - Seham Elaw Y1 - 2020/11/09 PY - 2020 N1 - https://doi.org/10.11648/j.ajcst.20200304.11 DO - 10.11648/j.ajcst.20200304.11 T2 - American Journal of Computer Science and Technology JF - American Journal of Computer Science and Technology JO - American Journal of Computer Science and Technology SP - 68 EP - 75 PB - Science Publishing Group SN - 2640-012X UR - https://doi.org/10.11648/j.ajcst.20200304.11 AB - Face detection is a common computer technology being used in human identification applications. It can also refer to the process of locating human faces in a visual scene. Face detection is a branched field of object detection where all objects in an image are detected including several classes like cars, trees, humans… etc. Also face detection problems branch into a lot of cases, some focus on frontal faces, others focus on side pose and so on. In this paper, a new face detection method based on Bilinear Interpolation image zooming method and image enhancement by Adaptive Histogram Equalization (AHE) method is proposed. The new method gives an encouraging results for crowded human images. By comparing the proposed method with the Viola-Jones algorithm, face detector using the cascade object detector, which supported in MATLAB, the new method gives excellent results in detecting human faces with different resolutions, poses and sizes. It succeeds in detecting most of the human faces in the tested images regardless of image sizes. The new method is tested on several images in Pratheepan dataset with crowded humans. Also, I tested the new method on many images collected from the Internet, whose can be classified as crowded human images. Experimental results show that the proposed Ad_L_Hist method is more efficient in detecting human faces in crowded human images. VL - 3 IS - 4 ER -
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