Open source biometric recognition

The biometrics community enjoys an active research field that has produced algorithms for several modalities suitable for real-world applications. Despite these developments, there exist few open source implementations of complete algorithms that are maintained by the community or deployed outside a laboratory environment. In this paper we motivate the need for more community-driven open source software in the field of biometrics and present OpenBR as a candidate to address this deficiency. We overview the OpenBR software architecture and consider still-image frontal face recognition as a case study to illustrate its strengths and capabilities. All of our work is available at www.openbiometrics.org.

[1]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[2]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Stan Z. Li,et al.  The HFB Face Database for Heterogeneous Face Biometrics research , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[5]  Klaus J. Kirchberg,et al.  Robust Face Detection Using the Hausdorff Distance , 2001, AVBPA.

[6]  Bruce A. Draper,et al.  Average of Synthetic Exact Filters , 2009, CVPR.

[7]  Anil K. Jain,et al.  Face Recognition Performance: Role of Demographic Information , 2012, IEEE Transactions on Information Forensics and Security.

[8]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  徐繼聖.  從Face Recognition Vendor Test (FRVT) 2006看臉部辨?科技近期的考驗 , 2006 .

[10]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[11]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[12]  Anil K. Jain,et al.  Face recognition across time lapse: On learning feature subspaces , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[13]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[14]  Bruce A. Draper,et al.  Preliminary studies on the Good, the Bad, and the Ugly face recognition challenge problem , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[15]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Jian Sun,et al.  Face recognition with learning-based descriptor , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Craig I. Watson,et al.  NIST special database 32 multiple encounter dataset II (MEDS-II) :: data description document , 2009 .

[18]  Amit R.Sharma,et al.  Face Photo-Sketch Synthesis and Recognition , 2012 .

[19]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[20]  Nicholas M. Orlans,et al.  NIST Special Databse 32 - Multiple Encounter Dataset II (MEDS-II) , 2011 .

[21]  Bruce A. Draper,et al.  An introduction to the good, the bad, & the ugly face recognition challenge problem , 2011, Face and Gesture 2011.

[22]  Xiaogang Wang,et al.  Coupled information-theoretic encoding for face photo-sketch recognition , 2011, CVPR 2011.

[23]  Carl E. Rasmussen,et al.  The Need for Open Source Software in Machine Learning , 2007, J. Mach. Learn. Res..

[24]  Patrick Grother,et al.  Face Recognition Vendor Test (FRVT) , 2014 .

[25]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.