Home security system based on Fuzzy k-NN Classifier

A Fuzzy k-nn Classifier for home security system we describe in this paper. Images were taken in uncontrolled indoor environment using video cameras of various qualities. Database contains 4,005 static images (in visible and infrared spectrum) of 267 subjects. Images from different quality cameras should mimic real-world conditions and enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios. In addition to database description, this paper also elaborates on possible uses of the database and proposes a testing protocol. A baseline Principal Component Analysis (PCA) face recognition algorithm was tested following the proposed protocol based on k-nn Classifier. Other researchers can use these test results as a control algorithm performance score when testing their own algorithms on this dataset. Database is available to research community through the procedure described at http://www.lrv.fri.uni-lj.si/facedb.html.

[1]  Joseph Wilder,et al.  Comparison of visible and infra-red imagery for face recognition , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[2]  F. Prokoski History, current status, and future of infrared identification , 2000, Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640).

[3]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[4]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[5]  I. Pavlidis,et al.  Thermal imaging for anxiety detection , 2000, Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640).

[6]  Pradeep Buddharaju,et al.  Pose-Invariant Physiological Face Recognition in the Thermal Infrared Spectrum , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[7]  Moulay A. Akhloufi,et al.  Thermal Faceprint: A New Thermal Face Signature Extraction for Infrared Face Recognition , 2008, 2008 Canadian Conference on Computer and Robot Vision.

[8]  Michael Harville,et al.  Foreground segmentation using adaptive mixture models in color and depth , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[9]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[10]  Ioannis Pavlidis,et al.  Thermal facial screening for deception detection , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[11]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[12]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[13]  Hiroshi Murase,et al.  Detection of 3D objects in cluttered scenes using hierarchical eigenspace , 1997, Pattern Recognit. Lett..

[14]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[15]  Keinosuke Fukunaga,et al.  Application of the Karhunen-Loève Expansion to Feature Selection and Ordering , 1970, IEEE Trans. Computers.

[16]  Somnath Sengupta,et al.  Human Motion Detection and Tracking for Video Surveillance , 2007 .

[17]  Masood Mehmood Khan,et al.  Automated Facial Expression Classification and affect interpretation using infrared measurement of facial skin temperature variations , 2006, TAAS.

[18]  Juha Karhunen,et al.  Generalizations of principal component analysis, optimization problems, and neural networks , 1995, Neural Networks.

[19]  Beat Fasel,et al.  Automati Fa ial Expression Analysis: A Survey , 1999 .

[20]  Takeo Kanade,et al.  Introduction to the Special Section on Video Surveillance , 2000, IEEE Trans. Pattern Anal. Mach. Intell..