2013 International Conference on Computer Communication and Informatics (ICCCI -2013), Jan. 04 – 06, 2013, Coimbatore, INDIA

978-1-4673-2907-1/13/$31.00 ©2013 IEEE

Skin Detection for Forensic Investigation

Varsha Powar MIT, Pune

Varsha78594@gmail.com

Renuka Lokare MMCOE, Pune

renu.lokare@gmail.com

Amruta Kulkarni MMCOE, Pune

amruta.k1401@gmail.com

Aishwarya Lonkar MMCOE, Pune

aishulonkar@gmail.com

Abstract—This Skin detection is process of extracting potential skin color regions from an image or video. Skin color detection restricts the work-space as it showcases human face, limbs and other exposed body features out of the whole image. Thus it can be used as a processing step in variety of applications like face detection and face recognition. Extraction of skin color pixels also help to identify pornographic image from the database of images and efficiently used for forensic purpose. It is been observed that there is positive correlation between images having large skin regions and the images which are pornographic in nature. We have implemented this approach using filtering of images in RGB and YCbCr color spaces using specific threshold values.

Keywords-YCbCr, RGB, Color Spaces

I. INTRODUCTION Forensic investigation is the cornerstone which

investigates for prohibiting usage of pornographic images and videos in media. Distinguishing pornographic images and video is important task as it avoids spreading of such images through internet. Identification of skin color region in color image is a preliminary step in various applications like video surveillance, face recognition in image. In our approach the method of detecting these images mainly focuses on skin region.

Marcus Roger [1] considered a combination of RGB and YCbCr filters. If the numbers of pixels in image are less than 20% then the image was discarded by RGB filter and it was not taken for further analysis. If RGB filter recognized more than 20% skin color pixels the then the image was further scanned by YCbCr filter. Now when windowing technique was applied on pixels which were detected as non skin pixels by RGB filter and skin pixels by YCbCr filter then the accuracy of the image was disturbed. H C Vijay Lakshmi [2] proposed a technique of skin detection in image using HSI and YCbCr color space. Various morphological operations such as erosion and dilation were carried out on skin segmented regions. The outputs of both the color spaces were combined together and connected component analysis was carried out. At the same time edge detection algorithms such as “Canny” and “Prewitt” were applied on gray scale image. Connected component

analysis and edge image results were multiplied in order to obtain region boundaries. Thus proposed approach avoids use of edge detection algorithm.

The rest of this paper is organized as follows Section II describes various color spaces discussed in paper. In section III proposed method is explained. Section V shows the experimental results.

II. COLOR SPACES Color information is an important aspect in image

processing and analysis. There are various color spaces available for detection of skin region in color image such as RGB, YCbCr, YUV, HSV, HSI, YIQ. In proposed approach a combination of RGB filter and YCbCr filter is used. RGB filter provides accurate results but has an effect of illumination. Due to the filter’s dependence on illumination, it could make error in detecting skin color pixels in image [2].

In order to improve the performance of skin color model YCbCr filter is inculcated as the effect of illumination can be completely removed by eliminating the Y component in color space. In order to detect skin color pixels a skin color map is derived and implemented on chrominance component of input image.

Although there is variation in human skin tone, human color varies less as compared to its brightness. According to various researches it has been observed that skin color has predominantly red color in comparison to the green and blue component. In examining the skin colors, blue color component does not contribute much to the color of skin. The red to green ratio seems to vary within the range of 2:1 to 3:1. Also it has been observed that skin color have red and green components approximately above the value of 40 on the scale of 0 to 255.

III. PROPOSED APPROACH Figure 1 gives overview of proposed system. The algorithm makes use of filters like RGB, YCbCr. We have also proposed a new filter for dark tone image. The full algorithm description is given below in five major steps.

2013 International Conference on Computer Communication and Informatics (ICCCI -2013), Jan. 04 – 06, 2013, Coimbatore, INDIA

978-1-4673-2907-1/13/$31.00 ©2013 IEEE

Each mentioned filter gives different results as skin tone and skin configuration varies from person to person. So in order to minimize this effect, the intersection is carried out on outputs of all the three filters, to detect missed out regions by previous filters.

Figure 1. Skin detection algorithm flowchart

We implemented the skin detection approach particularly for forensic purpose. The following are the steps and thresholds that we found out after applying the program to a set of images having pornographic as well as non-pornographic images. Step 1: The input image in RGB color space is taken. Step 2: RGB filter with following threshold is applied to the image. It is observed that RGB filter gives accuracy. But the color space also takes into consideration illuminations effect which is not desirable. We applied: R>95 & G>40 & B>20 |R-G|>15 & R>G & R>B Step3: The image is converted to YCbCr color space from RGB color space. To reduce the effects of illumination, we applied YCbCr filter. It does not give the result as accurate as RGB but by equating Y component to zero effect of illumination is eliminated. 76<Cb<128 & 132<Cr<174 Step 4: We found out that previous threshold were working only for fair, Asian skin toned people. Thus pornographic images having dark toned models were detected as non

pornographic. The skin of dark toned people shines in a photograph. Thus RGB filter cannot be used. We designed the following YCbCr threshold limits by applying them on a set of pornographic as well as non pornographic images of very dark toned people. Thus if an image passes as a ‘non-pornographic’ image from above two filter, this filter helps to rectify the error. 100<Cb<130 & 135<Cr<165 Step 5: Count the percentage of skin color pixels in all the above filters. If they lie in following limits the image is most probably pornographic.

G1>25 || G2>45 || G3>45 Where, G1, G2, G3 represent percentage of skin pixels obtained as a result of RGB filter, YCbCr filter, Filter for dark toned people respectively.

The level of security also plays an important role in deciding these limits. In some cases, not even a single part of the body is supposed to be exposed in the photograph. Then more strict limits are applied. But the above limits work fine with basic level of security.

IV. DISCUSSION

A. Challenges: • Non-pornographic image: If the photographs which

are zoomed on the face were having most of the portion occupied by skin color pixels. Thus even though it was non pornographic, the result came out to be pornographic (Type 2 error).

• Pornographic images: In few photographs, image is captured far away from the model. Thus skin pixel, even though the image is pornographic cover a very small part of the image. It causes the above approach to identify the image as non-pornographic (Type 1 error). This is not at all acceptable for forensic purpose.

B. Suggestions: • To avoid type 2 errors one can apply face detection

algorithms and boosting techniques, Haar feature extraction on the image to recognize the whole skin region as a face. But these errors, though passed as a pornographic image can be eliminated manually.

• To cover up type1 errors obtain the focus of image. If image is background oriented then separate out objects from background and apply object oriented skin analysis.

• Determining human bodies on a complex background image is a difficult task. To obtain human-like features we have to go for feature extraction and advanced algorithms for face detection like adaptive boosting algorithm.

Input image in RGB color space

RGB to YCbCr color

space

RGB filter

YCbCr filter

dark toned filter

Check for probable pornographic image

2013 International Conference on Computer Communication and Informatics (ICCCI -2013), Jan. 04 – 06, 2013, Coimbatore, INDIA

978-1-4673-2907-1/13/$31.00 ©2013 IEEE

V. EXPERIMENTAL RESULTS

For experimental result analysis we have taken images from web resources and digital camera. The images were analyzed in MATLAB environment. By performing the proposed model on set of 30 different images, statistical results are given below.

TABLE I. RESULT OF TEST IMAGES

TABLE II. RESULTS OF SKIN DETECTION ACCURACY

Parameters Output Generated Error Rate Total test images 30 20/30 Images correctly detected

20 20/30

Efficiency of code 66.67% 33.33%

Type 1 errors 2 6.67%

Type2 errors 8 26.67%

Convention used for errors: Type1 error: Pornographic image detected as non pornographic Type2 error: Non pornographic image detected as pornographic. Experiments depict that the proposed algorithm efficiently detects pornographic images. The type1 errors have very low error rate (6.6%) Thus almost all the images can be filtered without type1 error. Type 2 errors do not produce any harm to the forensic investigations. The error rate for type2 errors is also quite low (26.6%). These types of error are dependent on the focus of image, whether it is focused on the object or face or background. Type 1 errors mostly occur when object of interest i.e. human bodies cover a very small region in whole image. This case was observed for image I12 and similar type of images. Type2 errors frequently occurred when the image had a complete focus on faces. Thus faces covered maximum area of the image. In rare cases, complex background and skin colored objects like clothes, curtains, were responsible for these errors.

(a) Input Images

(b) Result after RGB filter

(c) Result after YCbCr filter

(d) Result after YCbCr for dark tone

(e) Intersection of above results

Figure 2. Result of image comparison

CONCLUSION This paper presents a methodical way to use skin detection

as a preliminary step for forensic purpose.

System searches entire image for skin colored pixels. The proposed work demonstrates the effect of RGB, YCbCr filters for images. The difficulty in skin color detection because of varying skin tones is also taken care of. Type1 and type2 errors regarding to pornographic nature of an image were defined and analysis of image set was done accordingly to evaluate error rate and efficiency of algorithms.

Image Porn image (Ideally)

Porn image (our result)

Error type

I1 False False 0 I2 False False 0 I3 True True 0 I4 False True 2 I5 True True 0 I6 False False 0 I7 True True 0 I8 False True 2 I9 True True 0 I10 False False 0 I12 False True 2 I13 True True 0 I14 False False 0 I15 False False 0 I16 True True 0 I17 True True 0 I18 True False 1

2013 International Conference on Computer Communication and Informatics (ICCCI -2013), Jan. 04 – 06, 2013, Coimbatore, INDIA

978-1-4673-2907-1/13/$31.00 ©2013 IEEE

While results of proposed method are not completely error-free and need a stronger check to overcome the limitations, the method reduces the type1 errors and gives 67% efficiency.

REFERENCES [1] Abhishek Choudhary ,Marcus Rogers and Blair Gillam," A novel skin

Detection Algorithm For Contraband image analysis", IEEE transactions,2008

[2] H C Vijay Lakshmi and S. Patil Kulkarni," Segmentation Algorithm For Multiple Face Detection In Color Images with Skin Tone Regions using Color Spaces and Detection Techniques", International Journal of Computer Theory and Engineering,1793-8201. 2010

[3] Hedieh Sajedi, Mehran Najafi and Shohreh Kasaei, “A boosted skin detection method based on pixel and block information”, Proceedings of the 5th International Symposium on image and Signal Processing and Analysis (2007)

[4] Yan- Wen wu and Xue- Yi Ai, “Face detection in color images using Adaboost algorithm based on skin color information”, IEEE transactions, 2008

[5] ”Digital Image Processing” by Gonzalez and Woods, Third edition, published by Pearson education.