COMPACT: Biometric Dataset of Face Images Acquired in Uncontrolled Indoor Environment

Biometric databases are important components that help to improve state-of-the-art recognition performance. The availability of more and more difficult data attracts the researchers' attention, who systematically develop novel recognition algorithms and increase identification accuracy. Surprisingly, most of the popular face datasets, like LFW or IJBA are not fully unconstrained. The majority of the available images were not acquired on-the-move, which reduces the amount of blur caused by motion or incorrect focusing. Therefore, in this paper, the COMPACT database for studying less-cooperative face recognition is introduced. The dataset consists of high-resolution images of 108 subjects acquired in a fully automated manner as people go through the recognition gate. This ensures that the collected data contains the real world degradation factors: different distances, expressions, occlusions, pose variations and motion blur. Additionally, the authors conducted a series of experiments that verify face recognition performance on the collected data.

[1]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Anil K. Jain,et al.  A Case Study of Automated Face Recognition: The Boston Marathon Bombings Suspects , 2013, Computer.

[3]  Elena Deza,et al.  Encyclopedia of Distances , 2014 .

[4]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  LinLin Shen,et al.  Face authentication test on the BANCA database , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[6]  Bruce A. Draper,et al.  The Good, the Bad, and the Ugly Face Challenge Problem , 2012, Image and Vision Computing.

[7]  Enrique Cabello.  Face biometrics without intrusion in airport , 2011, Proceedings of the International Conference on Optical Communication Systems.

[8]  Zoran Zivkovic,et al.  Improved adaptive Gaussian mixture model for background subtraction , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[9]  V. Lupu,et al.  Multimodal Biometrics for Access Control in an Intelligent Car , 2007, 2007 International Symposium on Computational Intelligence and Intelligent Informatics.

[10]  Damian Kacperski,et al.  Evaluation of head pose estimation methods for a non-cooperative biometric system , 2016, 2016 MIXDES - 23rd International Conference Mixed Design of Integrated Circuits and Systems.

[11]  Andrew Zisserman,et al.  Hello! My name is... Buffy'' -- Automatic Naming of Characters in TV Video , 2006, BMVC.

[12]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

[14]  Damian Kacperski,et al.  Calibration of vision systems operating in separate coordinate systems , 2016 .

[15]  Cordelia Schmid,et al.  Is that you? Metric learning approaches for face identification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[16]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[17]  Damian Kacperski,et al.  Pose-oriented face images acquisition platform , 2016, 2016 MIXDES - 23rd International Conference Mixed Design of Integrated Circuits and Systems.

[18]  Mislav Grgic,et al.  SCface – surveillance cameras face database , 2011, Multimedia Tools and Applications.

[19]  Hiroshi Sako,et al.  Image-recognition technologies towards advanced automated teller machines , 2004, ICPR 2004.

[20]  Hong Yan,et al.  Comparison of face verification results on the XM2VTFS database , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[21]  Davis E. King.  Max-Margin Object Detection , 2015, ArXiv.

[22]  Anil K. Jain,et al.  Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Tal Hassner,et al.  Face recognition in unconstrained videos with matched background similarity , 2011, CVPR 2011.

[25]  Yaniv Taigman,et al.  Descriptor Based Methods in the Wild , 2008 .

[26]  Yongkang Wong,et al.  Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition , 2011, CVPR 2011 WORKSHOPS.

[27]  Shree K. Nayar,et al.  Attribute and simile classifiers for face verification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[28]  Alice J. O'Toole,et al.  FRVT 2006 and ICE 2006 Large-Scale Experimental Results , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Stefan Winkler,et al.  A data-driven approach to cleaning large face datasets , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[30]  Masatoshi Ishikawa,et al.  High-speed gaze controller for millisecond-order pan/tilt camera , 2011, 2011 IEEE International Conference on Robotics and Automation.

[31]  Bruce A. Draper,et al.  Overview of the Multiple Biometrics Grand Challenge , 2009, ICB.

[32]  Jian Sun,et al.  Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[34]  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).