Abstract
This paper proposes a method based on Binarized Statistical Image Features (BSIF) and Image Quality Assessment for palmprint anti-spoofing approach. Firstly, BSIF computes a binary code for each pixel by filters, whose basis vectors are learnt from natural images via independent component analysis. For palmprint, it provides more texture information than the features in the original image. Image Quality Assessments are suitable measures since the recaptured images have features of blur and less details. Secondly, a new feature vector is formed by the former feature vectors. Finally, a SVM classifier is trained to discriminate the live and fake palmprint image. We collect a new database using iphone5 and iphone5s, which is the first one for palmprint liveness detection. Experiments on this database show great efficiency and high accuracy.
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References
Socolinsky, D.A., Selinger, A., Neuheisel, J.D.: Face recognition with visible and thermal infrared imagery. Computer Vision and Image Understanding 91(1), 72–114 (2003)
Jee, H.K., Jung, S.U., Yoo, J.H.: Liveness detection for embedded face recognition system. International Journal of Biological and Medical Sciences 1(4), 235–238 (2006)
Pan, G., Sun, L., Wu, Z.: Liveness detection for face recognition. INTECH Open Access Publisher (2008)
Joshi, T., Dey, S., Samanta, D.: Multimodal biometrics: state of the art in fusion techniques. International Journal of Biometrics 1(4), 393–417 (2009)
Kim, G., Eum, S., Suhr, J.K., et al.: Face liveness detection based on texture and frequency analyses. In: International Conference on Biometrics, pp. 67–72 (2012)
Ghiani, L., Marcialis, G.L., Roli, F.: Fingerprint liveness detection by local phase quantization. In: International Conference on Pattern Recognition, pp. 537–540 (2012)
Ghiani, L., Hadid, A., Marcialis, G.L., et al.: Fingerprint liveness detection using binarized statistical image features. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6 (2013)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)
Ahonen, T., Rahtu, E., Ojansivu, V., et al.: Recognition of blurred faces using local phase quantization. In: International Conference on Pattern Recognition, pp. 1–4 (2008)
Wu, L., Xu, X., Cao, Yu., Hou, Y., Qi, W.: Live face detection by combining the fourier statistics and LBP. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds.) CCBR 2014. LNCS, vol. 8833, pp. 173–181. Springer, Heidelberg (2014)
Kannala, J., Rahtu, E.: Bsif: Binarized statistical image features. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 1363–1366 (2012)
Li, Q., Chan, P.P.: Fingerprint liveness detection based on binarized statistical image feature with sampling from Gaussian distribution. In: 2014 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), pp. 13–17 (2014)
Basri, R., Jacobs, D.W.: Lambertian reflectance and linear subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(2), 218–233 (2003)
Li, J., Wang, Y., Tan, T., et al.: Live face detection based on the analysis of fourier spectra. In: Defense and Security, pp. 296–303. International Society for Optics and Photonics (2004)
Galbally, J., Marcel, S.: Face anti-spoofing based on general image quality assessment. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 1173–1178 (2014)
Sayood, K.: Statistical evaluation of image quality measures. Journal of Electronic Imaging 11(2), 206–223 (2002)
Huynh-Thu, Q., Ghanbari, M.: Scope of validity of PSNR in image/video quality assessment. Electronics Letters 44(13), 800–801 (2008)
Yao, S., Lin, W., Ong, E., et al.: Contrast signal-to-noise ratio for image quality assessment. In: IEEE International Conference on Image Processing. ICIP 2005, pp. I-397–400 (2005)
Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Transactions on Communications 43(12), 2959–2965 (1995)
Tan, X.Y., Li, Y., Liu, J., et al.: Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: European Conference on Computer Vision, pp. 504–517 (2010)
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Li, X., Bu, W., Wu, X. (2015). Palmprint Liveness Detection by Combining Binarized Statistical Image Features and Image Quality Assessment. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_33
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DOI: https://doi.org/10.1007/978-3-319-25417-3_33
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