Human 3D Reconstruction and Identification Using Kinect Sensor

3D model of human body has been widely used in many applications including medical, health and security, since it is able to provide information on human body shape. This paper proposes a method to identify human based on the 3D model of the body and the depth data form the Kinect. The system firstly utilizes the coordinate points from the 3D model to calculate the selected anthropometry features of human body. Then, the features are compared with real time Kinect's depth acquisition to perform pose recognition and human identification. Eight candidates were involved in the reliability test of the system with each of them performed 6 trials, making a total of 48 trials. The overall reliability of the system in identifying the correct candidate was found to be 79.167%.

[1]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[2]  Olivier D. Faugeras,et al.  Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score , 2007, International Journal of Computer Vision.

[3]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera , 2011, UIST.

[4]  S. C. Aung,et al.  Evaluation of the laser scanner as a surface measuring tool and its accuracy compared with direct facial anthropometric measurements. , 1995, British journal of plastic surgery.

[5]  Ricardo Matsumura de Araújo,et al.  Full Body Person Identification Using the Kinect Sensor , 2014, 2014 IEEE 26th International Conference on Tools with Artificial Intelligence.

[6]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[7]  Arun Ross,et al.  An introduction to biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Jiping Li,et al.  Personalized full-body reconstruction based on single kinect , 2015, 2015 8th International Congress on Image and Signal Processing (CISP).

[9]  Arun Ross,et al.  Fingerprint matching using minutiae and texture features , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[10]  Zhengyou Zhang,et al.  Microsoft Kinect Sensor and Its Effect , 2012, IEEE Multim..

[11]  Jun Cheng,et al.  Registration for 3-D point cloud using angular-invariant feature , 2009, Neurocomputing.

[12]  Larry S. Davis,et al.  Stride and cadence as a biometric in automatic person identification and verification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[13]  Mark S. Nixon,et al.  A floor sensor system for gait recognition , 2005, Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05).

[14]  Andrew E. Johnson,et al.  Surface registration by matching oriented points , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[15]  J. Daugman.  5 RECOGNIZING PERSONS BY THEIR IRIS PATTERNS , 2005 .

[16]  John Daugman,et al.  Recognising persons by their iris patterns , 2004, Defense + Commercial Sensing.

[17]  A. Peterkova,et al.  Obtaining the gait parameters from Kinect sensor for the person identification , 2015, 2015 IEEE 19th International Conference on Intelligent Engineering Systems (INES).

[18]  Edmond Boyer,et al.  Fusion of multiview silhouette cues using a space occupancy grid , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[19]  Trevor Darrell,et al.  A Bayesian approach to image-based visual hull reconstruction , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[20]  Sumanth Sakkara,et al.  Gait Recognition using skeleton data , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[21]  S. Mohan,et al.  Construction of 3D models from single view images: A survey based on various approaches , 2011, 2011 International Conference on Emerging Trends in Electrical and Computer Technology.

[22]  Tomás Pajdla,et al.  3D with Kinect , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[23]  Jiping Li,et al.  3D human body modeling based on single Kinect , 2014, 2014 7th International Conference on Biomedical Engineering and Informatics.

[24]  Kiriakos N. Kutulakos,et al.  A Theory of Shape by Space Carving , 2000, International Journal of Computer Vision.

[25]  Chia-Ling Tsai,et al.  The dual-bootstrap iterative closest point algorithm with application to retinal image registration , 2003, IEEE Transactions on Medical Imaging.

[26]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[27]  Kingshuk Chakravarty,et al.  Pose Based Person Identification Using Kinect , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[28]  Camillo J. Taylor.  Surface reconstruction from feature based stereo , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[29]  Ioannis Stamos,et al.  Integrating Automated Range Registration with Multiview Geometry for the Photorealistic Modeling of Large-Scale Scenes , 2008, International Journal of Computer Vision.