ABSTRACT
In this paper, we propose the use of periocular skin texture as a biometric modality. Salient skin texture features are extracted and represented using Local Binary Patterns (LBPs). Matching is performed using CityBlock distance as a measure of similarity. We investigate the use of each periocular region separately in addition to their use in conjunction. Verification and identification experiments involving over 400 subjects were performed using a datasets constructed from the FRGC and FERET datasets. Reported recognition rates of nearly 90%, demonstrate the effectiveness of this novel technique.
- G. Heusch, Y. Rodriguez, and S. Marcel. Local binary patterns as an image preprocessing for face authentication. Proc. of International Conference on Automatic Face and Gesture Recognition, pages 9--14, 2006. Google ScholarDigital Library
- H. Jin, Q. Liu, H. Lu, and X. Tong. Face detection using improved lbp under bayesian framework. Third International Conference on Image and Graphics (ICIG), pages 306--309, 2004. Google ScholarDigital Library
- S. Z. Li, R. Chu, M. Ao, L. Zhang, and R. He. Highly accurate and fast face recognition using near infrared images. Proc. Advances in Biometrics, International Conference, Lecture Notes in Computer Science 3832, Springer, pages 151--158, 2006. Google ScholarDigital Library
- H. Lian and B. liang Lu. Multi-view gender classification using local binary patterns and support vector machines, tsallis entropies and global appearance features. Proc. 3rd International Symposium on Neural Networks (ISNN), pages 202--209, 2006. Google ScholarDigital Library
- S. Liao, W. Fan, A. Chung, and D. Yeung. Facial expression recognition using advanced local binary patterns, tsallis entropies and global appearance features. Proc. of the IEEE International Conference on Image Processing (ICIP), pages 665--668, 2006.Google ScholarCross Ref
- T. Ojala, M. Pietikäinen, and D. Harwood. A comparative study of texture measures with classification based on feature distribution. Pattern Recognition, 29(1):51--59, 1996.Google ScholarCross Ref
- T. Ojala, M. Pietikäinen, and T. Mäenpää. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 24(7):971--987, 2002. Google ScholarDigital Library
- P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek. Overview of face recognition grand challenge. IEEE Conference on Computer Vision and Pattern Recognition, 2005. Google ScholarDigital Library
- P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss. The feret evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence, 22:1090--1104, 2000. Google ScholarDigital Library
- P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss. The feret database and evaluation procedure for face recognition algorithms. Image and Vision Computing, 16(5):295--306, 1998.Google ScholarCross Ref
- M. Savvides, R. Abiantun, J. Heo, C. Xie, and B. K. Vijayakumar. Partial and holistic face recognition on frgc ii using support vector machines. Proc. of IEEE Computer Vision Workshop (CVPW), page 48, 2006. Google ScholarDigital Library
- C. Shan, S. Gong, and McOwan. Robust facial expression recognition using local binary patterns. Proc. of the IEEE International Conference on Image Processing (ICIP), pages 370--373, 2005.Google Scholar
- N. Sun, W. Zheng, C. Sun, C. Zou, and L. Zhao. Gender classification based on boosting local binary patterns. International Symposium on Neural Networks (ISNN), pages 194--201, 2006. Google ScholarDigital Library
- Z. Sun, T. Tan, and X. Qiu. Graph matching iris image blocks with local binary pattern. Proc. Advances in Biometrics, International Conference (ICB), Lecture Notes in Computer Science 3832, Springer, pages 366--373, 2006. Google ScholarDigital Library
- X. Wang, H. Gong, H. Zhang, B. Li, and Z. Zhuang. Palmprint identification using boosting local binary pattern. Proc. 18th International Conference on Pattern Recognition (ICPR), 2006. Google ScholarDigital Library
- H. Zhang and D. Zhao. Spatial histogram features for face detection in color images. Advances in Multimedia Information Processing - PCM 2004: 5th Pacific Rim Conference on Multimedia, pages 377--384, 2004. Google ScholarDigital Library
- W. Zhang, S. Shan, H. Zhang, W. Gao, and X. Chen. Multi-resolution histograms of local variation patterns (mhlvp) for robust face recognition. Proc. Audio- and Video-Based Biometric Person Authentication: 5th International Conference (AVBPA), Lecture Notes in Computer Science 3546, Springer, pages 937--944, 2005. Google ScholarDigital Library
- Personal identification using periocular skin texture
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