Natural hand gestures for human identification in a Human-Computer Interface

The goal of this work is the identification of humans based on motion data in the form of natural hand gestures. The identification problem is formulated as classification with classes corresponding to persons' identities, based on recorded signals of performed gestures. The identification performance is examined with a database of twenty-two natural hand gestures recorded with two types of hardware and three state-of-art classifiers: Linear Discrimination Analysis (LDA), Support Vector machines (SVM) and k-Nearest Neighbour (k-NN). Results show that natural hand gestures allow for an effective human classification.

[1]  Q. Tian,et al.  Comparison of statistical pattern-recognition algorithms for hybrid processing. II: Eigenvector-based algorithm , 1988 .

[2]  Stefan Kopp,et al.  Systematicity and Idiosyncrasy in Iconic Gesture Use: Empirical Analysis and Computational Modeling , 2009, Gesture Workshop.

[3]  Frédéric Bevilacqua,et al.  Gesture Analysis of Violin Bow Strokes , 2005, Gesture Workshop.

[4]  Michal Romaszewski,et al.  Choosing and Modeling the Hand Gesture Database for a Natural User Interface , 2011, Gesture Workshop.

[5]  Deyou Xu.  A Neural Network Approach for Hand Gesture Recognition in Virtual Reality Driving Training System of SPG , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[6]  Nidal S. Kamel,et al.  A Sensor-Based Approach for Dynamic Signature Verification using Data Glove , 2008 .

[7]  Venu Govindaraju,et al.  Behavioural biometrics: a survey and classification , 2008, Int. J. Biom..

[8]  Seong-Whan Lee.  Automatic gesture recognition for intelligent human-robot interaction , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[9]  Xiaogang Wang,et al.  Random sampling LDA for face recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[10]  Richard Bowden,et al.  Sign Language Recognition , 2011, Visual Analysis of Humans.

[11]  Paolo Dario,et al.  A Survey of Glove-Based Systems and Their Applications , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[12]  Rashid Ansari,et al.  Multimodal human discourse: gesture and speech , 2002, TCHI.

[13]  Dong Yoon Kim,et al.  Two-stage Recognition of Raw Acceleration Signals for 3-D Gesture-Understanding Cell Phones , 2005 .

[14]  Kongqiao Wang,et al.  Hand gesture recognition and virtual game control based on 3D accelerometer and EMG sensors , 2009, IUI.

[15]  Walter D. Leon-Salas,et al.  A comparative study of classification methods for gesture recognition using a 3-axis accelerometer , 2011, The 2011 International Joint Conference on Neural Networks.

[16]  Soo-Young Lee,et al.  On-Line Handwritten Character Recognition with 3D Accelerometer , 2006, 2006 IEEE International Conference on Information Acquisition.

[17]  D. McNeill.  Hand and Mind: What Gestures Reveal about Thought , 1992 .

[18]  Rama Chellappa,et al.  Gait Analysis for Human Identification , 2003, AVBPA.

[19]  Hervé Lahamy,et al.  REAL-TIME HAND GESTURE RECOGNITION USING RANGE CAMERAS , 2010 .

[20]  Géraldine Damnati,et al.  Robust speech/non-speech detection using LDA applied to MFCC , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[21]  Hong Va Leong,et al.  Real-time tracking of hand gestures for interactive game design , 2009, 2009 IEEE International Symposium on Industrial Electronics.

[22]  Luis Mateus Rocha,et al.  Singular value decomposition and principal component analysis , 2003 .

[23]  Antonio Adán,et al.  Biometric verification/identification based on hands natural layout , 2008, Image Vis. Comput..

[24]  Hyeran Byun,et al.  Applications of Support Vector Machines for Pattern Recognition: A Survey , 2002, SVM.

[25]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  S. Mitra,et al.  Gesture Recognition: A Survey , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[27]  Helman Stern,et al.  Sensors for Gesture Recognition Systems , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[28]  Nasir D. Memon,et al.  Biometric-rich gestures: a novel approach to authentication on multi-touch devices , 2012, CHI.

[29]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[30]  Zhen Wang,et al.  uWave: Accelerometer-based Personalized Gesture Recognition and Its Applications , 2009, PerCom.

[31]  R. Bhavani,et al.  Performance Comparison of SVM and kNN in Automatic Classification of Human Gait Patterns , 2012 .

[32]  Piotr Gawron,et al.  Eigengestures for Natural Human Computer Interface , 2011, ICMMI.

[33]  Jitendra Malik,et al.  SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[34]  Klaus Hechenbichler,et al.  Weighted k-Nearest-Neighbor Techniques and Ordinal Classification , 2004 .