Face detection in color images

Hsu, Rein-Lien; Abdel-Mottaleb, Mohamed; Jain, Anil K.

doi:10.1109/icip.2001.959228

Cited by 22 publications

(16 citation statements)

References 13 publications

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“…Rein -Lien Hsu [1] found that people with different skin color can present a clustering feature in YCbCr space through the experimental analysis, as shown in Figure 1. Among them, the blue represent the point which can be simulated by the display, the red represent the point in the skin color area.…”

Section: Based On the Statistical Static Skin Color Space Modelmentioning

confidence: 99%

A Method of Gesture Segmentation Based on Skin Color and Background Difference Method

Duan¹,

Luo²

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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Abstract-This paper proposes a method of segmentation of hand gesture, based on the static skin color model and background subtraction under complex background. This paper discussed the reasonable threshold selection of Cb, Cr in the skin color model and the segmentation selection of skin area combining the background segmentation. Finally, an inequality of hand's outline feature is proposed to complete the division processing of the palm part of the area of skin. Experiments show the accurate segmentation of gesture under a complex static background.

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Section: Based On the Statistical Static Skin Color Space Modelmentioning

confidence: 99%

A Method of Gesture Segmentation Based on Skin Color and Background Difference Method

Duan¹,

Luo²

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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“…Among these, the class of methods based on invariant characteristics such as skin color, whose analysis turns out to be a good basic tool for multiple systems of face detection [2][3][4][5][6].…”

Section: Despite the Advancement Of Research In This Area Robust Facementioning

confidence: 99%

“…The HSV color space is used frequently too in some works [6,15]. Finally, note that other papers [2,5,[16][17][18] argue that the YCbCr color space provides good detection of skin pixels.…”

Section: Despite the Advancement Of Research In This Area Robust Facementioning

confidence: 99%

“…Including:  Skin parametric model which adjusts the parameters of the distribution using some specific functions such as the Gaussian [20], a Gaussian mixture or an elliptical model [2,18].  Nonparametric skin model which estimates the distribution of skin colors in the training data without any assumption about the distribution, such as Bayesian model based on histograms [10].…”

Section: Despite the Advancement Of Research In This Area Robust Facementioning

confidence: 99%

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Face Detection Algorithm based on Skin Detection, Watershed Method and Gabor Filters

Hajraoui¹,

Sabri²

2014

IJCA

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Automatic face detection has been intensively studied for human-related recognition systems. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. In this paper, a new face detection algorithm is proposed. This speedy and robust solution developed, on the one hand is based on the segmentation of the color image to skin regions using a new approach to detect the pixels of the skin and the water shed segmentation method. On the other hand, using Gabor filters, combined with a proposed model of face, skin regions are classified into two classes: face and non-face. The integration of these tools in our algorithm permits to develop a face detector with very reasonable and efficient performances. Experimental results show that the method mentioned in this paper can achieve high detection rates and low false positives. To evaluate the detection speed of proposed algorithm, a comparison with a recent known algorithm is made too.

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“…Note that face recognition is normally performed on grayscale (i.e., black and white) face images rather than color. Colors, and skin color tones in particular, are frequently used to aid face detection and location within the image stream (Rein-Lien, Abdel-Mottaleb, and Jain, 2002). We assume additionally that the face images are pre-processed by scaling, rotation, eye centre alignment, and background suppression so that averaging is meaningful.…”

Section: Principal Components Analysismentioning

confidence: 99%

Robust Face Recognition for Data Mining

Lovell¹,

Chen²,

Shan³

Encyclopedia of Data Warehousing and Mining, Second Edition

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INTRODUCTIONWhile the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them -and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21 st century, postproduction of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the internet backbone. BACKGROUNDAlthough they say a picture is worth a thousand words, computer scientists know that the ratio of information contained in images compared to text documents is often much greater than this. Providing text labels for image data is problematic because appropriate labeling is very dependent on the typical queries users will wish to perform, and the queries are difficult to anticipate at the time of labeling.For example, a simple image of a red ball would be best labeled as sports equipment, a toy, a red object, a round object, or even a sphere, depending on the A valuable prototype application that could be deployed on IrisNet is wide area person recognition and location services. Such services have existed since the emergence of human society to locate specific persons when they are not in immediate view. For example, in a crowded shopping mall, a mother may ask her child, "Have you seen your sister?" If there were a positive response, this may then be followed by a request to know the time and place of the last sighting, or perhaps by a request to go look for her. Here the mother is using the eyes, face recognition ability, memory persistence, and mobility of the child to perform the search. If the search fails, the mother may then ask the mall manager to give a "lost child"announcement over the public address system. Eventually the police may be asked to employ these human search services on a much wider scale by showing a photograph of the missing child on the television to ask the wider community for assistance in the search.On the IrisNet the mother could simply upload a photograph of her child from the image store in her mobile phone and the system would efficiently look for the child in an ever-widening geographic search space until contact was made. Cle...

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Face detection in color images

Cited by 22 publications

References 13 publications

A Method of Gesture Segmentation Based on Skin Color and Background Difference Method

A Method of Gesture Segmentation Based on Skin Color and Background Difference Method

Face Detection Algorithm based on Skin Detection, Watershed Method and Gabor Filters

Robust Face Recognition for Data Mining

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