Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images

Terrillon, Jean-Christophe; Shirazi, M.N.; Fukamachi, Hideo; Akamatsu, Shigeru

doi:10.1109/afgr.2000.840612

Cited by 322 publications

(187 citation statements)

References 20 publications

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“…Skin detection plays an important role in various applications such as face detection [13], searching and filtering image content on the web [15] [14]. Research has been performed on the detection of human skin pixels in color images and on the discrimination between skin pixels and "non-skin" pixels by use of various statistical color models.…”

Section: Introductionmentioning

confidence: 99%

Maximum Entropy Models for Skin Detection

Jedynak¹,

Zheng

Daoudi

2003

Lecture Notes in Computer Science

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We consider a sequence of three models for skin detection built from a large collection of labelled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model, called the baseline model is well known from practitioners. Pixels are considered as independent. Performance, measured by the ROC curve on the Compaq Database is impressive for such a simple model. However, single image examination reveals very irregular results. The second model is a Hidden Markov Model which includes constraints that force smoothness of the solution. The ROC curve obtained shows better performance than the baseline model. Finally, color gradient is included. Thanks to Bethe tree approximation, we obtain a simple analytical expression for the coefficients of the associated maximum entropy model. Performance, compared with previous model is once more improved.

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Section: Introductionmentioning

confidence: 99%

Maximum Entropy Models for Skin Detection

Jedynak¹,

Zheng

Daoudi

2003

Lecture Notes in Computer Science

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“…This choice of the color space was motivated by [23] and by our own empirical studies. Due to the large number of the color spaces available, we were not able to experiment with all of them.…”

Section: Resultsmentioning

confidence: 99%

Skin color-based video segmentation under time-varying illumination

Sigal,

Sclaroff,

Athitsos

2004

IEEE Trans. Pattern Anal. Machine Intell.

229

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“…Figure 7). In future work, we are planning to exploit other color spaces suitable for skin-color modeling, as for example proposed by [46].…”

Section: New Features Layersmentioning

confidence: 99%

Fast Face Detector Training Using Tailored Views

Scherbaum

Petterson

Feris

et al. 2013

2013 IEEE International Conference on Computer Vision

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Face detection is an important task in computer vision and often serves as the first step for a variety of applications. State-of-the-art approaches use efficient learning algorithms and train on large amounts of manually labeled imagery. Acquiring appropriate training images, however, is very time-consuming and does not guarantee that the collected training data is representative in terms of data variability. Moreover, available data sets are often acquired under controlled settings, restricting, for example, scene illumination or 3D head pose to a narrow range. This paper takes a look into the automated generation of adaptive training samples from a 3D morphable face model. Using statistical insights, the tailored training data guarantees full data variability and is enriched by arbitrary facial attributes such as age or body weight. Moreover, it can automatically adapt to environmental constraints, such as illumination or viewing angle of recorded video footage from surveillance cameras. We use the tailored imagery to train a new many-core implementation of Viola Jones' AdaBoost object detection framework. The new implementation is not only faster but also enables the use of multiple feature channels such as color features at training time. In our experiments we trained seven view-dependent face detectors and evaluate these on the Face Detection Data Set and Benchmark (FDDB). Our experiments show that the use of tailored training imagery outperforms state-of-the-art approaches on this challenging dataset.

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Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images

Cited by 322 publications

References 20 publications

Maximum Entropy Models for Skin Detection

Maximum Entropy Models for Skin Detection

Skin color-based video segmentation under time-varying illumination

Fast Face Detector Training Using Tailored Views

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