Face detection in color images using skin color model algorithm based on skin color information

Chandrappa, D N; Ravishankar, M.; Rameshbabu, D. R.

doi:10.1109/icectech.2011.5941600

Cited by 37 publications

(23 citation statements)

References 5 publications

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“…In this case, the MD from color vector to both centriod of skin cluster and non-skin cluster should be considered: Useing MCC as a metrics for evaluation, they conclude that CIE-Lab has the best performance while normalized rgb has the lowest. In [157,158], the SGM classifier is constructed based on rgb color space, and it is applied to remove probable non-regions of interests.…”

Section: ) Parametric Modelsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

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Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

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Section: ) Parametric Modelsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

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“…This paper presents a technique to improve feature based face detection introducing human skin color (HSC) characteristic. A number studies are available on human skin color based face detections [5][6][7][8][13][14][15][16][17][18][19][20][21][22][23][24][25]. The methods analyzed different color spaces (e.g., RGB, YCbCr, HIS, TSL, HSV) and their main focus was to generate a rule with the help of these color spaces which can determine whether a color is similar to human skin color or not.…”

Section: Open Access Research Articlementioning

confidence: 99%

Improvement of Haar Feature Based Face Detection in OpenCV Incorporating Human Skin Color Characteristic

MAH¹

2016

JCSAIT

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Open Access Research articledetection method detects facial features and ignores anything else, such as building, trees and also bodies. However, many face detection method fail to detect correct faces from images. David, Kriegman, and Ahuja presented a survey of face detection and presented the trends of researches in face detection [4]. In the survey authors categorized and evaluated different face detection algorithms. Some limitations of those algorithms were also discussed in a brief. A common problem of the existing methods is that they treat non facial area as a facial area. The popular Haar like feature based face detection [9,12] also suffers from the same problem. This paper presents a technique to improve feature based face detection introducing human skin color (HSC) characteristic. A number studies are available on human skin color based face detections [5][6][7][8][13][14][15][16][17][18][19][20][21][22][23][24][25]. The methods analyzed different color spaces (e.g., RGB, YCbCr, HIS, TSL, HSV) and their main focus was to generate a rule with the help of these color spaces which can determine whether a color is similar to human skin color or not. Different studies have also shown different techniques to model human skin color. In this study, HSC property has been incorporated with the popular Haar Feature Based Face Detection (HFFD) in OpenCV, and found to improve its performance.The rest of the paper is organized as follows. Section II explains HAAR feature based face detection method. Section III presents the proposed face detection technique incorporating of human skin color analysis in HAAR. Section IV presents experimental results to identify the proficiency of the proposed method. Section V concludes the paper with a brief summary. Haar Feature Based Face Detection in OpenCVOpenCV is a very popular tool for object detection. Any types of objects including human faces can be detected by it. Currently OpenCV is using Haar feature based cascaded classifier for face detection [10]. At first the classifier is trained with a lot of positive images (the images containing particular object like car or face we are interested to detect) scaled to same size say 20x20 resolution. And then the classifier is trained with some negative images (arbitrary images that does not contain that particular object like car or face) of same size. After completion of the training process the classifier capture frequently happening AbstractFace detection from a digital image or video stream is used often for various purposes. But sometimes a system detects an object or area as a face where there is no face at all. This paper presents a technique to reduce such wrong detection rate introducing human skin color (HSC) characteristic. The general property of human skin in RGB color space is that it possess R>G>B (i.e., red values are higher than green value and green value is higher than blue). In this study, such HSC property has been incorporated with the popular Haar Feature Based Face Detection (HFFD) in OpenCV, to reduc...

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“…To separate the location of the face from the background, the face detection has been done by the following processes: Skin likely-hood model, Morphological operations and Euler test [15,16].…”

Section: Face Detectionmentioning

confidence: 99%

Development of a geometrical algorithm for eye detection in color images

Phromsuthirak

Umchid

2012

The 5th 2012 Biomedical Engineering International Conference

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The applications of eye detection have grown significantly during the past decade. The objective of this paper was to develop a new technique for eye detection in color facial images. The implementation of this technique was composed of three steps. First step is using skin color information to detect a face in color images. The face region would be segmented from the background. Next step, illumination based method (chrominance components and luma component) was employed to find the possible location of the eyes. Finally, to locate the exact position of the eyes, the flexible thresholding method and new eye geometrical tests were utilized. The proposed technique was tested using Aberdeen facial images from Psychological Image Collection at Stirling (PICS image database). The simulation results indicate the detection rate of 93.2% for frontal facial images and 85.7% for rotational facial images.

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Face detection in color images using skin color model algorithm based on skin color information

Cited by 37 publications

References 5 publications

A Comprehensive Survey on Human Skin Detection

A Comprehensive Survey on Human Skin Detection

Improvement of Haar Feature Based Face Detection in OpenCV Incorporating Human Skin Color Characteristic

Development of a geometrical algorithm for eye detection in color images

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