Novel framework for segmentation of skin regions using chromatic and textural information

Then all MSDM is obtained by performing change procedure. based on similarities of the initial moving skin model with the FSDM and the FBDM. Eventually,. the static and the dynamic modules are merged by following a maximization rule. analysis shows that the proposed skin detection method a detet skin region is more aurate. than the modern methods when algechromativarations of skin occur.

Skin Segmentation

Colour spaes for skin detetion
RGB olour spae
TV olour spaes

YIQ
YCbCr
YUV

Pereptual olour spaes
Colorimetri olour spaes

Some of the main reasons for using YCbCr color space in skin segmentation are given as. One of the major advantages of using these color spaces for skin detection is that the .

Figure 1.2: Density plots of Asian skin for dierent olor spaes [1℄

Classiers for Skin Detetion

Histogram model with Naïve Bayes lassier
Gaussian lassiers

Single Gaussian model
Gaussian mixture model

Elliptial boundary model
Artiial neural network
Support vetor mahine
Random forest

A k-means gloss of the training data and provides the initial parameter values for EM. In this, X is the i'th characteristic chrominane vector and fi is the number of samples that have.

Evaluation Proedure

Evaluation metris

Datasets

Overview of Dierent Methods of Skin Detetion

Skin detetion methods using stati framework

Expliit boundary speiation
Parametri modelling
Non-parametri modelling

Another approach follows the use of spatial analysis of skin regions by exploiting the spatial extent .. of skin pixels and their relation to neighboring pixels.

Skin detetion methods using dynami arhiteture

Researh Motivation

In this method, afaedetion-based model update scheme. considered, and the method is not suitable for local illumination changes, as mostly ours. it is necessary to detect skin areas on an unknown image. ii). It is observed that despite having color deviation due to poor or uneven lighting.

Objetives

Thesis Organization

It is adaptive in a sense. that ADA adapts to similarities between classes or overlapping samples. function would be spei image, not generic for all images. In our approach, two groups of pixel locations S 1 and S 2 are used to derive the ADA projection feature. lotions of pixels that are most likely skin, which means that some of them can be seen. skin, but they actually belong to non-skin regions. The other set S 2 contains pixel positions. which are mostly without skin, but some of them belong to general skin regions. no labeled training data.

Subsequently, a discriminative feature space is extracted from the given image itself using the proposed ADA.

Proposed Method

Disriminative spae map

It is observed that skin regions possess higher SPM values, as was done with In some of the assets, the elderly S may have skin color as non-skin acne due to skin-. Finally two binary images are obtained from these two lustersuh that,. the model is taken from training skin samples for SPM extraction. model the distribution to be modeled using GMM as:. where, ω Skin k is the prior probability weight, K Skin is the number of Gaussians used for it.

GMM, µ Vel k is the mean, and Σ Vel k ovarian matrix of the GaussianG µ Vel k , Σ Vel k. Similarly, the distribution of pixels in RGB domain for the ith binary image is BW i an be. using a single Gaussian function. ber of skin pixels is significantly large compared to non-skin pixels in S old in some. It is observed that these baking soil regions. also includes a significant amount of image boundary pixels. keep real-skin pixels are selected based on the following decision rule:. In our approach, membership value of asample is. calculated with the mean intra-luster distances and mean inter-luster distances.

To show the importance of our proposed method, the discriminant maps obtained from Kawu-. from this analysis, it is quite clear that Kawulok's discriminating map is not adaptive to it. depends on sene harateristis. The national skin mask for a certain image can be easily obtained with the direct threshold of.

Figure 2.2 shows the blok diagram of proposed method for obtaining skin sample set S 1 for

Dynami region growing

To obtain more discrimination between skin and skin-like non-skin regions, loations {x : SP M (x) < 0.1} are discarded under the alulation of DSM max and the inverted DSM. As described by Kawulok [14℄, the fixed cheese to obtain a non-visited pixel x depends on two .. factors: a) cheese originated in the color domain and b) cheese originated in the DSM domain. Regardless of how uncontrolled environments are, background regions will be physically and texturally similar, and e.g. Ases, DSM itself a does not perfetly discriminate skin and non-skin regions.

The skin chroma entropy is calculated for the RGB color space and is denoted as E s.

Figure 2.4: Illustration of skin hroma entropy as a measure of possible false aeptane error: a)

Experimental Results

Experimental setup

Experimental validation

If most of the training samples for two girls are well discriminated due to texture. To test the eay of DRG, ost maps are derived from SPM using the proposed. plots for different methods for both HGR and ECU datasets are shown in Figures 2.10-2.11. It is clear from the ROC clock that the optimal operating region for. of different methods are compared qualitatively with the results of our proposed method. this, a set of sample images is seleted from HGR and ECU datasets.

Figure 2.5: Variation of δ t with β in dierent datasets

Summary

Proposed Method

Global skin probability map

Image pixel distribution model

For this purpose, a singularity hack of the ovarian arteries is performed at each iteration.

F usion-based skin probability map

FSPM derivation by using GSDM
FSPM derivation by using FSDM

These glosses can have a significantly lower number of patterns. spei, the M-step of the EM-algorithm for GMM is modified to suit our purpose. If the o-variance matrix does not reach singularity hek, then it is. algorithm, then the final value of the Gaussian components K I is obtained. Therefore, the skin similarity index (d GI S,i) and the skinless similarity index (d GI N S,i) are the ith.

These are the actual distribution models for an image's skin and non-skin pixels. This can result in a large amount of spurious aeptane errors. Obviously, the color of the skin resembles the skin color of a person. A measurement must be made between the IDM and a model describing a defective skin pixel. the skin-like colored background areas are more eaten. the elf is located using the algorithm proposed by Viola and Jones [125℄. the skin pixels are determined using a loalfae model derived from the skin pixels. variations in the skin areas of different body parts due to non-uniform lighting. observation is that despite minor color variations, the overall color properties of. the skin areas do not droop significantly. So IDM's shine with real skin pixels. will statistically be the loser of the loalfae model.

The proposed skin and skin dissimilarity indices are used to derive the distribution models for both skin and non-skin. But it also assigns higher probability values to the skin areas or colors that resemble faial.

Figure 3.1: Illustration of GMM modelling of image pixel distribution: a) Original image, b) Image

Experimental Analysis

GSPM is observed to give relatively better background aura presence. similarity, but performs poorly in low light. Therefore, the parameter ρ is chosen to be 0.5 for. validation of F SP M N oF ace and kept fixed for all datasets. However, the Cambridge dataset images fall into a special situation where illumination. non-uniform and skin-like colors are present in the background. colors would be present in the background.

Now, regional cultivation-based methods reduce the number of false positives by as much. an increased false negative error rate. The problem of the false positive error rate applies to them. rich in skin-like baking ground colours. So for these two datasets the region is growing. based methods detect skin areas with relatively fewer overall detection errors. in HGR datasets were taken under poor but fairly uniform lighting conditions.

Thus, region growing methods are also more efficient in the HGR dataset. The comparative ROC plots for all datasets are in Figure 3.6 and Figure 3.7. a comparative analysis of efficiency is also shown in Figure 3.8. eaof datasets are selected to display the detection results. GSDM compared to actual skin areas due to poor lighting. the skin distribution model must be adapted to the skin color of the person present in the image.

Figure 3.5: δ t vs β for dierent datasets

Summary

In addition to the actual skin areas, some background areas may also be.

Proposed Method

Stati module

F aial skin distribution model
Bakground Distribution model

Gaussian G I components sent to regions of real skin should be statistically more similar to FSDM and should differ more from Gaussian om-. Patch P 1 and P patch 3 belong to skin regions, while patch P 2 belongs to non-skin regions. We believe that P patch 1 has actual skin tone and P patch 3 has deformed skin tone due to loal.

The weights of the Gaussian components in G lb are derived from the weights they had in G I .

Figure 4.2: Similarity between two lusters.

Dynami module

Key frame seletion
Moving skin distribution model

FOA our if the object is without texture and/or image intensity gradient. So the Q motion pixel set can be used to derive a color distribution model for moving skin regions, ie. of the moving skin distribution model (MSDM).

Figure 4.4: Drawbak of single frame dierene method.

Derivation of a skin mask

Experimental Analysis

Experimental setup

Determination of τ

Experimental validation

On the other hand, the average false negative error δfn,avg decreases. significantly for an increase in φ max from 0 ◦ to 20 ◦. the average total detection error δ t,avg for all videos is the lowest. SPM for region growth, and therefore it cannot detect many real skin pixels.

Figure 4.11: Comparative bar plots for dierent videos: (a) δ t for dierent videos, and (b) Auray

Summary

A modified version of the Bhattharyya distana is proposed to better resemble the mea-. Assurance between the unknown skin distribution and the reference skin distribution. A summary of all the chapters of this dissertation is highlighted as follows: .. i) Chapter 1 gives a brief description of a typical skin detection method and its various components such as useful dye solutions and lasers. Existing skin detection techniques should be grouped into the following groups. two main categories: a) approaches based on a statistical framework and b) a dynamics framework.

On the . On the other hand, dynami framework-based methods were mainly developed for videos when . The lighting varies between frames, globally or locally. The methods can be further grouped into the following three subcategories based on their approaches to exhaustion: a) explicit boundary representation of color components, b) skin. lassia using parametric modeling of skin color distribution, and) skin lassia using non-parametric modeling of skin color distribution. separation of color components is the simplest and fastest approach of all. these approaches. All these approaches are discussed in detail in Chapter 1. Chapter 1 also discusses the challenges of skin detection. Including the presence of skin-like colors in the background, romati distortion of the skin due to non-uniform or time-varying exposures. objectives and organization of the dissertation. ii) Chapter 2 uses the development of a method for extracting distinguishing features between them. The performance of the proposed method is evaluated using standard. databases, and it is noted that the proposed method produces a better result than . state-of-the-art methods. iii) Chapter 3 explores the use of image pixel distribution information in skin text.

The proposed dynamic region growing (DRG) algorithm is applied to the FSPM for a . more aurate skin detection. Experimental results show that the proposed method detects. skin areas in images sharper than the most modern methods. iv) Chapter 4 describes a skin detection method for videos with color distortion.

F uture W ork

Skin segmentation: a) original image, b) skin mask and ) segmented image

Density plots of Asian skin for dierent olor spaes [1℄

Density plots of Asian, Afrian and Cauasian skin for dierent olour spaes [1℄ 6

Skin detetion methods

Skin olour distributions in dierent olour planes: (a) T sekeridou and Pitas [2℄,

Skin detetion using ANN proposed by Seow et al. [11℄

Proposed framework by Yang et al. [12℄

Blok diagram of the proposed skin detetion algorithm

Proposed method for obtaining the set S 1

Experimental results showing disrimination between skin and non-skin pixels

ROC urves for dierent methods: a) Cambridge dataset, b) NUS dataset