Volume 3, Issue 4, December 2020, Page: 68-75
Face Detection in Crowded Human Images by Bi-Linear Interpolation and Adaptive Histogram Equalization Enhancement
Seham Elaw, Department of Computer Science, Faculty of Computers and Information, Sohag University, Sohag, Egypt
Received: Oct. 11, 2020;       Accepted: Oct. 28, 2020;       Published: Nov. 9, 2020
DOI: 10.11648/j.ajcst.20200304.11      View  35      Downloads  29
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.
Keywords
Face Detection, CIE_Lab Color Space, Bilinear Interpolation, Adaptive Histogram Equalization
To cite this article
Seham Elaw, Face Detection in Crowded Human Images by Bi-Linear Interpolation and Adaptive Histogram Equalization Enhancement, American Journal of Computer Science and Technology. Vol. 3, No. 4, 2020, pp. 68-75. doi: 10.11648/j.ajcst.20200304.11
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Liu H. (2015), Face Technologies on Mobile Devices in Facial Detection and Recognition on Mobile Devices, Elsevier Inc, doi: http://dx.doi.org/10.1016/B978-0-12-417045-2.00001.
[2]
Divyarajsinh N., Parmar, Brijesh B. and Mehta, Face Recognition, Methods & Applications (2014), International Journal of Computer Technology & Applications, vol. 4 (1), pp. 84-86.
[3]
Chang Y., Jung C., Ke P., Song H. and Hwang J. (2018), Automatic Contrast-Limited Adaptive Histogram Equalization With Dual Gamma Correction, in IEEE Access, vol. 6, pp. 11782-11792, doi: 10.1109/ACCESS.2018.2797872.
[4]
Chiang Ch., Chen K., Chu Ch., Chang Y. and Fan K. (2018), Color Enhancement for Four-Component Decomposed Polarimetric SAR Image Based on a CIE-Lab Encoding, Remote Sens., 10, 545; doi: 10.3390/rs10040545. www.mdpi.com/journal/remotesensing, pp. 2-17.
[5]
Alionte E. and Lazar C. (2015), A Practical Implementation of Face Detection by Using MATLAB Cascade Object Detector, 19th International Conference on System Theory, Control and Computing (ICSTCC), October 14-16, Cheile Gradistei, Romania, 978-1-4799-8481-7/15/$31.00 ©2015 IEEE, pp. 785-790.
[6]
Thamizharasi A. and Jayasudha J. S. (2016), An Illumination Invariant Face Recognition by Enhanced Contrast Limited Adaptive Histogram Equalization, Intact Journal on Image and Video Processing, Volume: 06, Issue: 04, pp. 1258-1266. DOI: 10.21917/ijivp.2016.0183.
[7]
Magudeeswaran V., Fenshia Singh J. (2017), Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images, International Journal of Imaging Systems and Technology, vol. 27, pp. 98–103, https://doi.org/10.1002/ima.22214.
[8]
https://en.wikipedia.org/wiki/Bilinear_interpolation last accessed 7-9-2020.
[9]
https://thilinasameera.wordpress.com/2010/12/24/digital-image-zooming-sample-codes-on-matlab/ last accessed 2-10-2020.
[10]
https://en.wikipedia.org/wiki/Adaptive_histogram_equalization#:~:text=Adaptive%20histogram%20equalization%20(AHE)%20is,to%20improve%20contrast%20in%20images.&text=It%20is%20therefore%20suitable%20for,each%20region%20of%20an%20image last accessed 27-9-2020.
[11]
https://en.wikipedia.org/wiki/CIELAB_color_space#searchInput last accessed 7-9-2020
[12]
http://www.brucelindbloom.com/index.html?Math.html last accessed 10-9-2020.
[13]
https://en.wikipedia.org/wiki/CIELAB_color_space last accessed 10-9-2020.
[14]
https://www.mathworks.com/help/vision/ref/vision.cascadeobjectdetector-system-object.html last accessed 11-9-2020.
[15]
https://www.mathworks.com/help/vision/ug/train-a-cascade-object-detector.html#:~:text=from%20one%20side.-,The%20vision.,its%20aspect%20ratio%20remains%20fixed. last accessed 10-9-2020.
[16]
https://thilinasameera.wordpress.com/2010/12/24/digital-image-zooming-sample-codes-on-matlab/#_Nearest_Neighbour_Interpolation last accessed 7-9-2020.
[17]
Tan W. R., Chan C. S., Y. Pratheepan and Condell J. (2012.), A Fusion Approach for Efficient Human Skin Detection Condell, IEEE Transactions on Industrial Informatics, vol. 8 (1), pp. 138-147, doi: 10.1109/TII.2011.2172451.
[18]
http://cs-chan.com/downloads_skin_dataset.html last accessed 27-9-2020.
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