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.
Highlights
Face detection is the first step in many face recognition applications such as automated control systems
By comparing with the face detection function supported in MATLAB, which depends on the Viola-Jones algorithm and face detector using the cascade object detector, the new method gives excellent results in detecting human faces with different resolution, poses and sizes
I tested the new method on many images collected from the Internet, whose can be classified as crowded human images
Summary
Face detection is the first step in many face recognition applications such as automated control systems It has a lot of challenges, represented in variations of image appearance, illumination conditions, occlusions, image orientation and face expressions. Photo taking is the most useful application of face detection, when you take some photos to yourself and your friends in a journey, for example, the algorithm of face detection in your camera detects the human faces in the taken photo and adjust the focus [1] Another very useful application for face detection is security cameras in universities, airport offices, banks, ATM and any location with a security system [2]. A lot of researches concerned on several color spaces like HSV, RGB, YCrCb, CIE-Lab and CIE-YIQ to detect skin like color range
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