Biometric Data Analysis

Slides (pdf)Video

Abstract

Biometric data is everywhere with fast development of mobile and wearable devices, social media, surveillance networks and identification systems. Biometric data analysis can obtain a wide variety of information including identity, gender, ethnicity, age and affect from biometric data. This talk will present a review of main approaches for biometric data analysis. However, there are still many open problems in biometric data analysis. Compared with biometric identification, there is relatively less research on demographic and affective information prediction from biometric data.


Biography

TAN Tieniu, Professor of computer vision and pattern recognition, is Vice President of the Chinese Academy of Sciences. He is Member of the Chinese Academy of Sciences, International Fellow of the UK Royal Academy of Engineering, Fellow of The World Academy of Sciences for the advancement of sciences in developing countries (TWAS), Corresponding Member of the Brazilian Academy of Sciences, and Fellow of the IEEE and IAPR (International Association for Pattern Recognition).

TAN received his BSc in electronic engineering from Xi'an Jiaotong University, China in 1984, and his MSc and PhD degrees in electronic engineering from Imperial College London, UK in 1986 and 1989 respectively.

In October 1989, he joined the Department of Computer Science, The University of Reading, U.K., where he worked as Research Fellow, Senior Research Fellow and Lecturer. In January 1998, he returned to China to join the National Laboratory of Pattern Recognition (NLPR), CAS Institute of Automation as a full professor. He was the Director General of the Institute from 2000-2007, Director of the NLPR from 1998-2013 and Deputy Secretary-General of the Chinese Academy of Sciences from 2007-2015. He is currently Director of the Center for Research on Intelligent Perception and Computing at the Institute of Automation. He has published more than 500 research papers in refereed international journals and conferences in the areas of image processing, computer vision and pattern recognition, and has authored or edited 11 books. He holds more than 70 patents. His current research interests include biometrics, image and video understanding, and information forensics and security.


References

  • D.Wang, C.Otto and A.K Jain, "Face Search at Scale: 80 Million Gallery", arXiv, July 28, 2015.
  • Hu Han, Charles Otto, Xiaoming Liu and Anil K. Jain, "Demographic Estimation from Face Images: Human vs. Machine Performance", IEEE Trans. PAMI, vol.37, no.6, pp.1148-1161, 2015.
  • Vince Thomas, Nitesh V. Chawla, Kevin W. Bowyer, and Patrick J. Flynn, "Learning to Predict Gender from Iris Images", in Proc. IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp.1–5, 2007.
  • Stephen Lagree and Kevin W. Bowyer, "Predicting ethnicity and gender from iris texture ", in Proc. IEEE International Conference on Technologies for Homeland Security, pp.440–445, 2011.
  • A. Bansal, R. Agarwal, and R.K. Sharma, "Predicting Gender Using Iris Images", Research Journal of Recent Sciences, vol.3, no.4, pp.20–26, 2014.
  • Juan E. Tapia, Claudio A. Perez and Kevin W. Bowyer, "Gender Classification From Iris Images Using Fusion of Uniform Local Binary Patterns", Lecture Notes in Computer Science. Springer, vol. 8926, pp. 751–763, 2015.
  • Mark A. Acree, "Is there a gender difference in fingerprint ridge density?", Forensic Science International, vol.102, no.1, pp.35-44, 1999.
  • N. Kapoor and A. Badiye, "Sex Differences in the Thumbprint Ridge Density in a Central Indian Population", Egyptian Journal of Forensic Sciences, vol.5, no.1, pp:23-29, 2015.
  • V. C. Nayak, et al., "Sex Differences from Fingerprint Ridge Density in Chinese and Malaysian Population", Forensic Science International, vol.197, no.1-3, pp:67-69, 2010.
  • E. B. Ceyhan and S. Sagiroglu, "Gender Inference within Turkish Population by Using Only Fingerprint Feature Vectors", IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, 2014.
    G. A. Eshak, et al., "Sex Identification from Fingertip Features in Egyptian Population", Journal of Forensic and Legal Medicine, vol.20, no.1, pp: 46-50, 2013.
  • E. Gutiérrez-Redomero, et al, "Sex Differences in Fingerprint Ridge Density in The Mataco-mataguayo Population", HOMO - Journal of Comparative Human Biology, vol.62, no.6, pp: 487-499, 2011.
  • E. Gutiérrez-Redomero, et al. "A Comparative Study of Topological and Sex Differences in Fingerprint Ridge Density in Argentinian and Spanish Population Samples", Journal of Forensic and Legal Medicine, vol.20, no.5, pp: 419-429, 2013.
  • E. Gutiérrez-Redomero, et al., "Variability of Fingerprint Ridge Density in a Sample of Spanish Caucasians and Its Application to Sex Determination", Forensic Science International, vol.180, no.1, pp: 17-22, 2008.
  • Ahmed Badawi, Mohamed Mahfouz, Rimon Tadross and Richard Jantz, "Fingerprint-based Gender Classification", in Proc. International Conference on Image Processing, Computer Vision, pp. 41–46, 2006.
  • Xiong Li, Xu Zhao, Yun Fu and Yuncai Liu, "Bimodal Gender Recognition from Face and Fingerprint", in Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2590–2597, 2010.
  • Samta Gupta and A. Prabhakar Rao, "Fingerprint Based Gender Classification Using Discrete Wavelet Transform & Artificial Neural Network", International Journal of Computer Science and Mobile Computing, pp. 1289–1296, 2014.
  • Eyup Burak Ceyhan and Seref Sagiroglu, "Gender Inference within Turkish Population by Using Only Fingerprint Feature Vectors", IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, pp. 146–150, 2014.
  • Gholamreza Amayeh, George Bebis and Mircea Nicolescu, "Gender Classification from Hand Shape", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.1-7, 2008.
  • Jiajia Lei, Jindan Zhou and Mohamed Abdel-Mottaleb. "Gender Classification Using Automatically Detected and Aligned 3D Ear Range Data", in Proc. International Conference on Biometrics, 2013.
    Guangpeng Zhang and Yunhong Wang. "Hierarchical and Discriminative Bag of Features for Face Profile and Ear Based Gender Classification", in Proc. International Joint Conference on Biometrics, 2011.
  • Shiqi Yu, Tieniu Tan, Kaiqi Huang, Kui Jia and Xinyu Wu, "A Study on Gait-Based Gender Classification", IEEE Transactions on Image Processing, vol.18, no.8, pp.1905-1910, 2009.
  • Ting Huang, Yingchun Yang and Zhaohui Wu, "Combining MFCC and Pitch to Enhance the Performance of the Gender Recognition", in Proc. International Conference on Signal Processing, pp.16-20, 2006.
  • Yu-Min Zeng, et al., "Robust GMM Based Gender Classification Using Pitch and RASTA-PLP Parameters of Speech", in Proc. Int. Conf. Mach. Learn. Cybern. pp. 3376-3379, 2006.
  • Yen-Liang Shue, et al., "The Role of Voice Source Measures on Automatic Gender Classification", in Proc. IEEE ICASSP, pp. 4493-4496, 2008.
  • Yingle Fan, et al., "Speaker gender identification based on combining linear and nonlinear features", in Proc. 7th WCICA. pp. 6745-6749, 2008.
  • Ting Huang , et al., "Combining MFCC and pitch to enhance the performance of the gender recognition", in Proc. 8th Int. Conf. Signal Process., 2006.
  • Deepak S. Deepawale, et al., "Energy estimation between adjacent formant frequencies to identify speaker ’s gender", in Proc. 5th Int. Conf. ITNG, pp. 772-776, 2008.
  • Florian Metze, et al., "Comparison of four approaches to age and gender recognition for telephone applications", in Proc. IEEE ICASSP, pp. IV-1089IV-1092, 2007.
  • Abdulaali Hassaine, et al., "ICDAR 2013 Competition on Gender Prediction from Handwriting", in Proc. International Conference on Document Analysis and Recognition, pp.1417-1421, 2013.
  • Somaya Al Maadeed and Abdelaali Hassaine, "Automatic Prediction of Age, Gender and Nationality in Offline Handwriting." EURASIP Journal on Image and Video Processing, vol.2004, no.1, pp.1-10, 2014.
  • Satoshi Hosoi, Erina Takikawa and Masato Kawade. "Ethnicity Estimation with FacialImages", in Proc. IEEE International Conference on Automatic Face and Gesture Recognition,2004.
  • Siyao Fu, Haibo He and Zeng-Guang Hou. "Learning Race from Face: A Survey", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, no.12, pp.2483-2509, 2014.
  • Xiaoguang Lu, Hong Chen and Anil K. Jain, "Multimodal facial gender and ethnicity identification", Advances in Biometrics. Springer Berlin Heidelberg, pp. 554-561, 2005.
  • Omar Ocegueda, et al., "3D Face Discriminant Analysis Using Gauss-markov Posterior Marginals", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, no.3, pp. 728–739, 2013.
  • Xianchao Qiu, Zhenan Sun and Tieniu Tan, "Learning appearance primitives of iris images for ethnic classification", in Proc.International Conference on Image Processing, vol. 2, pp. II–405–II–408, 2007.
  • Stephen Lagree and Kevin W. Bowyer, "Predicting Ethnicity and Gender from Iris Texture", in Proc. IEEE International Conference on Technologies for Homeland Security, pp. 440–445, 2011.
  • Hui Zhang, Zhenan Sun, Tieniu Tan and Jianyu Wang, "Ethnic Classification Based on Iris Images.", ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, vol. 7098, book section 11, pp. 82–90, 2011.
  • Anahita Zarei and Mou Duxing, "Artificial Neural Network for Prediction of Ethnicity Based on Iris Texture," in Proc. International Conference on Machine Learning and Applications, pp. 514–519, 2012.
  • Zhenan Sun, Hui Zhang, Tieniu Tan and Jianyu Wang, "Iris Image Classification Based on Hierarchical Visual Codebook." , IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, no.6, pp. 1120–1133, 2014.
  • De Zhang, Yunhong Wang and Bir Bhanu. "Ethnicity Classification Based on Gait Using Multi-view Fusion", in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.108-115, 2010.
  • D.A. Reynolds, "Overview of Automatic Speaker Recognition", JHU 2008 Workshop Summer School.
    Z. Zeng, M. Pantic, G.I. Roisman and T. S. Huang. "A survey of affect recognition methods: Audio, visual, and spontaneous expressions." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 1, pp. 39-58, 2009.
  • M. Li, K. J. Han, and S. Narayanan. "Automatic speaker age and gender recognition using acoustic and prosodic level information fusion."Computer Speech & Language, vol. 27, no.6, pp. 151-167, 2014.
  • K.P. Truong, and D. A. Van Leeuwen. "Automatic discrimination between laughter and speech." Speech Communication, vol.49, no.2, pp. 144-158, 2007.
  • Y. Makihara, H. Mannami and Y. Yagi, "Gait Analysis of Gender and Age Using a Large-Scale Multi-view Gait Database", In Proc. Asian Conference on Computer Vision, pp. 440-451, 2011.
  • P. Gnanasivam and D.S. Muttan. "Estimation of age through fingerprints using wavelet transform and singular value decomposition." International Journal of Biometrics and Bioinformatics, vol. 6, no. 2, pp. 58-67, 2012.
  • H.R. Lv, Z.L. Lin, W.J. Yin and J. Dong, "Emotion recognition based on pressure sensor keyboards.", in Proc. IEEE Internation Conference on Multimedia and Expo, 2008.
Tieniu Tan

Tieniu Tan
Chinese Academy of Sciences, China