Statistical Color Models with Application to Skin Detection

1. Statistical Color Models with Application to Skin Detection Michael J. Jones and James M. RehgCambridge Research LaboratoryCompaq Computer CorporationOne Cambridge Center International Journal of Computer Vision, Vol. 46, No. 1, 2002

2. Introduction • The paper describes the construction of skin and non-skin statistical color models; • Uses 1 billion training pixels from Web images; • Shows a skin classifier with accuracy of 88%; • System used to detected naked people on Web images; • Compares Histograms and MoG;

3. Histogram Color Models • Images are organized in two sets: • Generic Training Set; • Used to compute a general histogram density; • Classifier Training Set; • Used to build the skin and non-skin models; • Manually separated into subsets containing skin and not containing skin; • Skin pixels are manually labeled;

4. General Color Model • Observations: • Most colors fall on or near the gray line; • Black and white are by far the most frequent colors, with white occuring slightly more frequently; • There is a marked skew in the distribution toward the red corner of the color cube.

5. Skin and Non-skin Color Models • Observations: • Non-skin model is the general model without skin pixels (10% of pixels); • There is a significant degree of separation between the skin and non-skin models;

6. Skin Detection Using Color Models • Given skin and non-skin histogram models we can construct a skin pixel classifier; • Such a classifier is extremely useful in two contexts: • Detection and recognition of faces and figures; • Image indexing and retrieval;

7. P(rgb) skin non-skin rgb Skin Detection Using Color Models • a skin pixel classifier is derived through the standard likelihood ratio approach: x

8. Histogram-based Skin Classifier

9. Histogram-based Skin Classifier • Qualitative observations: • θ = 0.4; • The classifier does a good job of detecting skin in most of these examples; • In particular, the skin labels form dense sets whose shape often resembles that of the true skin pixels; • The detector tends to fail on highly saturated or shadowed skin;

10. Histogram-based Skin Classifier • More qualitative observations: • The example photos also show the performance of the detector on non-skin pixels. • In photos such as the house (lowerright) or flowers (upper right) the false detections are sparse and scattered. • More problematic are images with wood orcopper-colored metal such as the kitchen scene (upper left) or railroad tracks (lower left). • These photos contain colorswhich often occur in the skin model and are difficult to discriminate reliably. • This results in fairly dense sets of falsepostives.

11. Histogram-based Skin Classifier • Quantitative observations: fraction of pixels labeled as skin that were classified correctly. fraction of non-skin pixels which are mistakenly classified as skin.

12. Receiver Operating Characteristic Curves (ROC curves) Original Θ = 7 Θ = 9

13. Histogram-based Skin Classifier • More quantitative observations: • The performance of the skin classifier is surprisingly good considering the unconstrained nature of Web images; • The best classifier (size 32) can detect roughly 80% of skin pixels with a false positive rate of 8.5%, or 90% correct detections with 14.2% false positives; • Its equal error rate is 88%.

14. Comparison to Mixture of Gaussian Classifier • One advantage of mixture models is that they can be made to generalize well on small amounts of training data; • One possible benefit of a large dataset is the ability to use density models such as histograms which are computationally simpler to learn and apply. • Mixture models were trained for the dataset and compared their classification performance to the histogram models.

15. Comparison to Mixture of Gaussian Classifier • two separate mixture models were trained for the skin and non-skin classes; • It was used 16 Gaussians in each model; • The models were trained using a parallel implementation of the standard EM algorithm.

16. Comparison to Mixture of Gaussian Classifier

17. Comparison to Mixture of Gaussian Classifier

18. Comparison to Mixture of Gaussian Classifier • The mixture of Gaussian model is significantly more expensive to train than the histogram models; • It took 24 hours to train both skin and non-skin mixture models using 10 Alpha workstations in parallel. In contrast, the histogram models could be constructed in a matter of minutes on a single workstation; • The mixture model is also slower to use during classification since all of the Gaussians must be evaluated in computing the probability of a single color value; • In contrast, use of the histogram model results in a fast classifier since only two table lookups are required to compute the probability of skin. • From the standpoint of storage space, however, the mixture model is a much more compact representation of the data.

19. Comparison to Mixture of Gaussian Classifier

20. Applications • Person Detector; • Adult Image Detector; • Incorporating Text Features into Adult Image Detection.

21. Person Detector • The goal is to determine whether or not an input image contains one or more people; • A classifier is trained on a simple feature vector computed from the output of the skin detector; • Percentage of pixels detected as skin; • Average probability of the skin pixels; • Size in pixels of the largest connected component of skin; • Number of connected components of skin; • Percent of colors with no entries in the skin and non-skin histograms.

22. Person Detector • The goal is to determine whether or not an input image contains one or more people; • A decision tree classifier is trained on a simple feature vector computed from the output of the skin detector; • Percentage of pixels detected as skin; • Average probability of the skin pixels; • Size in pixels of the largest connected component of skin; • Number of connected components of skin; • Percent of colors with no entries in the skin and non-skin histograms. • Results: • 83.2% (correctly classified person images) • 71.3% (correctly classified non-person images) • 77.5% (correctly classified images)

23. Adult Image Detector • There is a strong correlation between images with large patches of skin and adult or pornographic images; • All of these services currently operate by maintaining lists of objectionable URL’s and newsgroups and require constant manual updating; • An image-based scheme has the potential advantage of applying equally to all images without the need for updating.

24. Adult Image Detector • The same approach as with person detection is used; • A neural network classifier is used; • The feature vector includes two additional elements corresponding to the height and width of the image; • These two were added based on informal observations that adult images are often sized to frame a standing or reclining figure.

25. Adult Image Detector • Results: • 85.8% correctly detected adult images • 7.5% mistakenly classified non-adult images

26. Adult Image Detector

27. Text Features into Adult Image Detection • Combining the adult image detector just described with a text-based classifier (obtained from AltaVista) which uses the text occurring on a Web page to determine if it is pornographic; • The text-based detector uses the standard approach of matching words on the page against a manually-specified list of objectional words, and classifying based on the match frequencies; • The text-based classifier on its own achieves 84.9% correct detections of adult images with 1.1% false positives.

28. Text Features into Adult Image Detection • The color-based detector is combined with the text-based detector by using an “OR” of the two classifiers, i.e. an image is labeled adult if either classifier labels it adult.

29. Conclusions • Color distributions for skin and non-skin pixel classes learned from web images can be used as an accurate pixel-wise skin detector; • The key is the use of a very large labeled dataset to capture the effects of the unconstrained imaging environment represented by web photos; • Visualization studies show a surprising degree of separability in the skin and non-skin color distributions; • They also reveal that the general distribution of color in web images is strongly biased by the presence of skin pixels.

30. Conclusions • One possible advantage of using a large dataset is that simple learning rules may give good performance; • A pixel-wise skin detector can be used to detect images containing naked people, which tend to produce large connected regions of skin; • It is shown that a detection rate of 88% can be achieved with a false alarm rate of 11.3%, using a seven element feature vector and a neural network classifier; • This performance is comparable to systems which use more elaborate and slower spatial image analysis; • The results suggest that skin color is a very powerful cue for detecting people in unconstrained imagery.