The document proposes a method to detect face spoofing using colour texture analysis. It discusses existing methods that focus on grayscale images and discard colour information. The proposed method analyzes texture and quality in RGB, HSV, and YCbCr colour spaces. Local binary patterns descriptors are extracted from each colour channel and concatenated into an enhanced feature vector to classify whether a face is real or fake. The method aims to improve upon existing techniques by leveraging both luminance and chrominance information from colour images.

1. FACE SPOOFING DETECTION
USING
COLOUR TEXTURE ANALYSIS

2. CONTENTS
• INTRODUCTION
• EXISTING METHOD
• PROPOSED METHOD
• ADVANTAGES
• APPLICATIONS
• CONCLUSION AND FUTURE SCOPE
• REFERENCES

3. INTRODUCTION
• Detection using colour texture analysis
• Information from luminanace and chrominance are collected
• Existing method focussed on analysis of luminanace
information of face images and discarding chroma component
• Observed fake image have lower image quality with lack of
high frequency information

4. Contd…
• Fake images are identified by analysing chroma component
than luminance
• Preliminary colour texture analysis approach is proposed
• Non intrusive software based detection focus on gray scale
images, discarding colour information

5. EXISTING METHOD
• Hardware based solution appoach
 Surface reflectance properties are used
 Thermal information are for detecting printed and replayed
video attacks
 But are intrusive, expensive or impractical since
unconventional imaging devices are required

6. Contd…
• Challenge response approach
 Specific action is choosed as challenge and actually performed
or not, is response
• Non intrusive approach
 No user co-operation is required
 Assessed by commonly used any database
 Categorized into static and dynamic tech

7. Contd…
 High resolution input image are required to extract fine details
 Generation capabilities are not clear due to lack of training and
testing set
 Colour local binary patterns descriptor is only used

8. PROPOSED METHOD
• Face spoofing attacks mostly performed by displaying using
prints, video displays or masks
• Detect by analysing texture and quality of captured gray scale
image
• Discriminating genuine faces from fake ones by insight image
into three colour spaces RGB, HSV, YCbCr

9. Contd…
• Performance of different facial colour texture representation is
compared to their gray scale

10. Contd…
• Similarity between LBP descriptions extracted from face 1 and
face 2 for printed and video attacks
• Similarity is measured using the chi-square distance
• Hx and Hy are two LBP histograms

11. • Chi-square distance between gray-scale LBP histograms of the
genuine face and the printed fake face is smaller than the one
between two genuine face images

12. Contd…
• Mean LBP histograms for both real and fake face images to
compute a Chi-square distance as
• Hx is the LBP histogram of test sample and Hr & Hf are the
reference histograms for real and fake faces

13. Contd…

14. • Score distributions of the real faces and spoofs in the gray-
scale and YCbCr colour space.
• Chi-square statistics of the real and fake face descriptions in
the gray-scale space and Y channel are overlapping
• Better separated in the chroma components of the YCbCr
space.
Contd…

15. Proposed face anti-spoofing approach

16. Contd…
• Face is detected, cropped and normalised into an M×N pixel
image
• Texture descriptions are extracted from each colour channel
• Resulting feature vectors are concatenated into an enhanced
feature vector to get an overall representation of the facial colour
texture
• Final feature vector is fed to a binary classifier
• Output score value describes whether there is a live person or a
fake one in front of the camera

17. Contd…
• Facial representations extracted from different colour spaces
using different texture descriptors can also be concatenated
• Colour space
 Two other colour spaces, HSV and YCbCr, to explore the
colour texture information in addition to RGB
 HSV colour space, hue and saturation dimensions define the
chrominance and while the value dimension corresponds to the
luminance

18. Contd…
 YCbCr space separates the RGB components into Y, Cb and
Cr
• Texture Descriptors
 Designed for gray- scale images can be applied on colour
images by combining the features extracted from different
colour channels
 5 descriptors

19. Contd…
 Local Binary Patterns (LBP)
• Binary code computed by thresholding
• Binary patterns are collected into histograms
 Co-occurrence of Adjacent Local Binary Patterns (CoALBP)
• LBP discards spatial information
• To exploit the spatial relation between patterns

20. Contd…
 Local Phase Quantization (LPQ)
• Deal with blurred image
• Phase information extracted by STFT to analyse
neighbourhood
• Quantized and collected into histograms
 Binarized Statistical Image Features (BSIF)
• Convolving the image with linear filter and binarizing filter
response

21. Contd…
 Scale-Invariant Descriptor (SID)
• Image is first re-sampled densely enough on a log-polar grid,
rotations and scalings in the original image domain
• Fourier transform is applied on the re-sampled image,
invariance to both scale and rotation is achieved

22. ADVANTAGES
• Do not require any additional sensor
• Focused on both printed and replayed video attacks
• Good generalization ability
• Low computational complexity
• Fast response
• CTA features are more robust

23. APPLICATIONS
• Authentication system
• Registration purpose
• Mobile payment
• Unlocking system
• Security purpose

24. CONCLUSION AND FUTURE SCOPE
• Approach the problem of face anti-spoofing from the colour
texture analysis
• Colour image representations can used for describing the intrinsic
disparities in colour texture
• Facial colour texture representations studied by extracting
different local descriptors
• Improving generalization capabilities of colour texture analysis
based face spoofing detection

25. REFERENCES
[1] Zinelabidine Boulkenafet, Jukka Komulainen, and Abdenour Hadid,”Face
Spoofing Detection Using Colour Texture Analysis”, IEEE Transactions On
Information Forensics And Security, Vol. 11, No. 8, August 2016.
[2] Y. Li, K. Xu, Q. Yan, Y. Li, and R. H. Deng, “Understanding OSN-based
facial disclosure against face authentication systems,” in Proc. 9th ACM Symp.
Inf., Comput. Commun. Secur. (ASIA CCS), 2014, pp. 413–424.
[3] J. Li, Y. Wang, T. Tan, and A. K. Jain, “Live face detection based on
the analysis of Fourier spectra,” Proc. SPIE, vol. 5404, pp. 296–303,
Aug. 2004.
[4] X. Tan, Y. Li, J. Liu, and L. Jiang, “Face liveness detection from a single
image with sparse low rank bilinear discriminative model,” in Proc. 11th Eur.
Conf. Comput. Vis., VI (ECCV), 2010, pp. 504–517.
[5] Z. Zhang, J. Yan, S. Liu, Z. Lei, D. Yi, and S. Z. Li, “A face antispoofing
database with diverse attacks,” in Proc. 5th IAPR Int. Conf. Biometrics (ICB),
Mar./Apr. 2012, pp. 26–31.