As part of International Conference on Communication and Signal Processing (ICCASP) at Dr. Babasaheb Ambedkar Technological University (BATU), Lonere, India. Paper presentation about a combinatorial approach towards skin detection

1. Human Skin Detection Using RGB,
HSV and YCbCr Color Models
Ms. Seema Kolkur, Dr. D. R. Kalbande
Mr. Prajwal Shimpi, Mr. Chaitanya Bapat, Ms. Janvi Jatakia

2. Presentation Flow
•Introduction
•Motivation / Purpose
•Problem or Challenge
•Color Models
•Proposed Algorithm
•Comparative Study
•Future Scope
•Conclusion
Slide 2

3. Introduction
• Skin Image Detection - Process of finding skin-
colored pixels and regions in an image or a video.
• Factors considered for Skin Image recognition
- Skin Color
- Skin Tone
- Texture
- Edges
Ultimate aim -
- Differentiate Skin Pixel from Non-skin Pixel
Slide 3

4. Purpose
This presentation
•Recommends a improvised algorithm for Human
Skin detection
•Highlights the advantages of combining the color
models for superior skin image recognition
Slide 4

5. Problem / Challenge
•Wide range of parameters for Skin Image
Recognition
•Problems involved in Skin Color thresholds
- Effect of illumination
- Individual characteristics such as Age, Sex
- Varying skin tone
- Background color, shadows
- Motion blur
Slide 5

6. Color Models
• RGB Color Model
- Used for storing digital
images
- Components(primary colours):
• Red
• Green
• Blue
- Pixel colour can be broken in
the components
Slide 6

7. Color Models (contd.)
•YCbCr Color Model
- Used in digital video domain
- Components :
• Luminance
• Chrominance
- Y gives Luminance
- Cb, Cr gives Chrominance
Slide 7

8. Color Models(contd.)
•HSV Color Model
• H - Hue counter-clockwise
angle
• S - Saturation
- High for Pure Colors
- Low for Gray Scale
• V - Value
Slide 8

9. Proposed Algorithm
•Load image and Divide into 2-d array
•Extract argb value
•Convert RGB into HSV color model value
•Convert RGB to YCbCr color model value
•Compare with the threshold
- Mask / Unmask
•Continue for all pixels in skin image
Slide 9

10. Proposed Algorithm
Slide 10
Load Image
Divide Image in
2D array
Access
first/next pixel
Extract argb
value
Convert rgb
to hsv
Convert rgb
to YCbCr
argb,hsv,ycbcr
equal to
standard value
of skin pixel
Do not mask
the pixel
Mask the pixel
with black
color
No
Yes

11. Experimental Results
• Pratheepan dataset for human skin detection is
used as baseline for comparing results.
• These images are captured with a range of different
cameras using different colour enhancement and
under different illuminations.
• Contains Ground Truth images for sample images
in dataset.
Slide 11

12. Experimental Results (contd.)
Slide 12
The figures below are the original images,ground truth images
and the output images

13. Experimental Results (contd.)
Slide 13
The figures below are the original images and the output images

14. Experimental Results
• True positive (TP) – Skin pixel correctly identified
as skin
• True negative (TN) – Non-skin pixel correctly
identified as non-skin
• False positive (FP) – Non-skin pixel incorrectly
identified as skin
• False negative (FN) – Skin pixel incorrectly
identified as non-skin
• Precision – TP / (TP + FP)
• Accuracy – TP + TN / (TP + TN + FP +FN)
Slide 14

15. Experimental Results (contd.)
Slide 15
Sr. No True
Positive
False
Positive
True
Negative
False
Negative
Precision Accuracy
1 54885 0 1472 951 100 99.5
2 23089 6739 1465 803 96.5 98.3
3 18926 402 1980 600 97.9 99.1

16. Experimental Results (contd.)
Slide 16

17. Future Scope
The future scope of this paper includes:
• Facial Recognition
• Hand Gesture Recognition as an aid for differently
abled
• Effective skin disease detection
Slide 17

18. Conclusion
Hence the algorithm stated in this paper
• Uses a hybrid approach by combining RGB,HSV
and YCbCr spaces
• Effectively recognises skin pixel in different light
conditions
• Exhibits greater accuracy and precision.
Slide 18