Comparison of five color models in skin pixel classification

Zarit, B.D.; Super, Boaz J.; Quek, Francis

doi:10.1109/ratfg.1999.799224

Cited by 226 publications

(114 citation statements)

References 16 publications

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“…It is reported that the normalized color spaces yield best results and are preferable for robust color based skin detection. In [34], five color spaces and two histogram based skin detection methods are considered. It is reported that HSV combined with lookup table method has higher skin detection performance using % skin correct measure and that color space transformation affects the over-all skin detection performance.…”

Section: Related Workmentioning

confidence: 99%

Color based skin classification

Khan

Hanbury

Stöttinger

et al. 2012

Pattern Recognition Letters

133

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Skin detection is used in applications ranging from face detection, tracking body parts and hand gesture analysis, to retrieval and blocking objectionable content. In this paper, we investigate and evaluate (1) the effect of color space transformation on skin detection performance and finding the appropriate color space for skin detection, (2) the role of the illuminance component of a color space, (3) the appropriate pixel based skin color modeling technique and finally, (4) the effect of color constancy algorithms on color based skin classification. The comprehensive color space and skin color modeling evaluation will help in the selection of the best combinations for skin detection. Nine skin modeling approaches (AdaBoost, Bayesian network, J48, Multilayer Perceptron, Naive Bayesian, Random Forest, RBF network, SVM and the histogram approach of Jones and Rehg [15]) in six color spaces (IHLS, HSI, RGB, normalized RGB, YCbCr and CIELAB) with the presence or absence of the illuminance component are compared and evaluated. Moreover, the impact of five color constancy algorithms on skin detection is reported. Results on a database of 8991 images with manually annotated pixel-level ground truth show that (1) the cylindrical color spaces outperform other color spaces, (2) the absence of the illuminance component decreases performance, (3) the selection of an appropriate skin color modeling approach is important and that the tree based classifiers (Random forest, J48) are well suited to pixel based skin detection. As a best combination, the Random Forest combined with the cylindrical color spaces, while keeping the illumi- nance component outperforms other combinations, and (4) the usage of color constancy algorithms can improve skin detection performance.

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Section: Related Workmentioning

confidence: 99%

Color based skin classification

Khan

Hanbury

Stöttinger

et al. 2012

Pattern Recognition Letters

133

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“…Furthermore, TSL is cyclic represented by the angle of color components, which may cause instabilities for naive distance computation. We suggest the performance drop of the Simplicity and explicit separation of luminance and chrominance components [25,46] and popularity in skin detection applications [18] difference features in comparison to the use of the original features is a consequence of the Hue representation in TSL and its relatives HSL and HSU were we observed the same effect. However, the adaptive distance is able to compensate for this effect and still yields a boost of performance also in these color spaces.…”

Section: Retrieval Ratementioning

confidence: 69%

“…In [40] the authors argue, that normalized TSL (Tint, Saturation, Lightness) is superior to other color spaces for skin modeling with a unimodal Gaussian joint probability density function. The color space YCrCb is adjusted for efficient image compression, but the transformation simplicity and explicit separation of luminance and chrominance components appear attractive for skin color modeling [25,46,9]. Surveys on color spaces and their use can be found in [40,43].…”

Section: Introductionmentioning

confidence: 99%

“…Color is an important attribute for primary skin efflorescences [3]. Color features have proven beneficial in many applications and medical sciences, especially for the recognition of skin lesions [14,40,43,38,34,19,37,25,46,18] or the classification of skin cancer [28,44,1,16,10,42]. A dermatologist might be interested in pictures of similar skin lesions in comparison to an actual case to verify the diagnosis or confer with similar symptoms.…”

Section: Introductionmentioning

confidence: 99%

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Learning effective color features for content based image retrieval in dermatology

Bunte

Biehl

Jonkman

et al. 2011

Pattern Recognition

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“…Consequently, there have been a number of attempts to determine the optimum color space for skin segmentation. Researchers have concluded that the skin color distribution forms a cluster (the so-called skin locus) in various color spaces [33], [34], which is however, camera-specific. For a more comprehensive discussion on skin color detection techniques, the reader is also referred to [35].…”

Section: Color-based Face Detectionmentioning

confidence: 99%

A monocular system for person tracking: Implementation and testing

Stamou

Krinidis

Nikolaidis

et al. 2007

J Multimodal User Interfaces

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This paper presents a complete functional system capable of detecting people and tracking their motion in either live camera feed or pre-recorded video sequences. The system consists of two main modules, namely the detection and tracking modules. Automatic detection aims at locating human faces and is based on fusion of color and feature-based information. Thus, it is capable of handling faces in different orientations and poses (frontal, profile, intermediate). To avoid false detections, a number of decision criteria are employed. Tracking is performed using a variant of the well-known Kanade-Lucas-Tomasi tracker, while occlusion is handled through a re-detection stage. Manual intervention is allowed to assist both modules if required. In manual mode, the system can track any object of interest, so long as there are enough features to track. The system caters for calibrated cameras and can provide 3-D coordinates of any tracked object(s) of interest. It has been tested with very good results on a variety of video sequences, including a database of studio video sequences, for which 3-D ground truth data, originating from a 4-camera infrared tracking system, exist.

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Comparison of five color models in skin pixel classification

Abstract: Detection of skin in video is

Cited by 226 publications

References 16 publications

Color based skin classification

Color based skin classification

Learning effective color features for content based image retrieval in dermatology

A monocular system for person tracking: Implementation and testing

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