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Face spoofing detection using texture analysis

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Face spoofing detection

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Face spoofing detection using texture analysis

  1. 1. FACE SPOOFING DETECTION USING COLOUR TEXTURE ANALYSIS
  2. 2. CONTENTS • INTRODUCTION • EXISTING METHOD • PROPOSED METHOD • ADVANTAGES • APPLICATIONS • CONCLUSION AND FUTURE SCOPE • REFERENCES
  3. 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. 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. 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. 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. 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. 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. 9. Contd… • Performance of different facial colour texture representation is compared to their gray scale
  10. 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. 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. 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. 13. Contd…
  14. 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. 15. Proposed face anti-spoofing approach
  16. 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. 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. 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. 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. 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. 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. 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. 23. APPLICATIONS • Authentication system • Registration purpose • Mobile payment • Unlocking system • Security purpose
  24. 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. 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.

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