Identity verification based on haptic handwritten signatures: Genetic programming with unbalanced data

In this paper, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. The relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification is investigated. In particular, several fitness functions are used and their comparative performance is investigated. They take into account the unbalance dataset problem (large disparities within the class distribution), which is present in identity verification scenarios. GP classifiers using such fitness functions compare favorably with classical methods. In addition, they lead to simple equations using a much smaller number of attributes. It was found that collectively, haptic features were approximately as equally important as visual features from the point of view of their contribution to the identity verification process.

[1]  Una-May O'Reilly,et al.  Genetic Programming II: Automatic Discovery of Reusable Programs. , 1994, Artificial Life.

[2]  B. Malek,et al.  Novel Shoulder-Surfing Resistant Haptic-based Graphical Password , 2006 .

[3]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[4]  Abdulmotaleb El-Saddik,et al.  Feature selection in haptic-based handwritten signatures using rough sets , 2010, International Conference on Fuzzy Systems.

[5]  Mark Johnston,et al.  Differentiating between individual class performance in Genetic Programming fitness for classification with unbalanced data , 2009, 2009 IEEE Congress on Evolutionary Computation.

[6]  Abdulmotaleb El-Saddik,et al.  Visualizing Human Behavioral Features based on Signature Haptic Data , 2008, 2008 Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems.

[7]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[8]  Abdulmotaleb El-Saddik,et al.  Relevant Feature Selection and Generation in High Dimensional Haptic-based Biometric Data , 2009, DMIN.

[9]  Ivo F. Sbalzariniy,et al.  Multiobjective optimization using evolutionary algorithms , 2000 .

[10]  Abdulmotaleb El-Saddik,et al.  A Novel Biometric System for Identification and Verification of Haptic Users , 2007, IEEE Transactions on Instrumentation and Measurement.

[11]  Mengjie Zhang,et al.  Fitness Functions in Genetic Programming for Classification with Unbalanced Data , 2007, Australian Conference on Artificial Intelligence.

[12]  Abdulmotaleb El-Saddik,et al.  Experiments in haptic-based authentication of humans , 2008, Multimedia Tools and Applications.

[13]  I. Tomek,et al.  Two Modifications of CNN , 1976 .

[14]  Abdulmotaleb El-Saddik,et al.  Feature selection and classification in genetic programming: Application to haptic-based biometric data , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[15]  Malcolm I. Heywood,et al.  GP Classification under Imbalanced Data sets: Active Sub-sampling and AUC Approximation , 2008, EuroGP.

[16]  Taghi M. Khoshgoftaar,et al.  Experimental perspectives on learning from imbalanced data , 2007, ICML '07.

[17]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[18]  Mark Johnston,et al.  A Comparison of Classification Strategies in Genetic Programming with Unbalanced Data , 2010, Australasian Conference on Artificial Intelligence.

[19]  Abdulmotaleb El-Saddik,et al.  Exploring the underlying structure of haptic-based handwritten signatures using visual data mining techniques , 2010, 2010 IEEE Haptics Symposium.

[20]  David B. Fogel.  What is evolutionary computation , 1995 .

[21]  A. El Saddik,et al.  Behavioral Features for Different Haptic-based Biometric Tasks , 2007, 2007 IEEE International Workshop on Haptic, Audio and Visual Environments and Games.

[22]  Mark Johnston,et al.  Genetic Programming for Classification with Unbalanced Data , 2010, EuroGP.

[23]  Julio J. Valdés,et al.  Exploring medical data using visual spaces with genetic programming and implicit functional mappings , 2007, GECCO '07.

[24]  B. Malek,et al.  Haptic-Based Sensible Graphical Password , 2007 .

[25]  A. El Saddik,et al.  Characterizing biometric behavior through haptics and Virtual Reality , 2008, 2008 42nd Annual IEEE International Carnahan Conference on Security Technology.

[26]  John R. Koza.  Genetic Programming III - Darwinian Invention and Problem Solving , 1999, Evolutionary Computation.

[27]  Abdulmotaleb El-Saddik,et al.  Haptic-Based Biometrics: A Feasibility Study , 2006, 2006 14th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems.

[28]  Michael J. Pazzani,et al.  Reducing Misclassification Costs , 1994, ICML.

[29]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[30]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.

[31]  John R. Koza,et al.  Hierarchical Genetic Algorithms Operating on Populations of Computer Programs , 1989, IJCAI.