A new hand-movement-based authentication method using feature importance selection with the hotelling’s statistic

• Autorzy: Doroz Rafal , Wrobel Krzysztof , Porwik Piotr , Orczyk Tomasz

Identyfikatory

DOI

10.2478/jaiscr-2022-0004

• Języki publikacji: EN

Abstrakty

EN

The growing amount of collected and processed data means that there is a need to control access to these resources. Very often, this type of control is carried out on the basis of biometric analysis. The article proposes a new user authentication method based on a spatial analysis of the movement of the finger’s position. This movement creates a sequence of data that is registered by a motion recording device. The presented approach combines spatial analysis of the position of all fingers at the time. The proposed method is able to use the specific, often different movements of fingers of each user. The experimental results confirm the effectiveness of the method in biometric applications. In this paper, we also introduce an effective method of feature selection, based on the Hotelling T2 statistic. This approach allows selecting the best distinctive features of each object from a set of all objects in the database. It is possible thanks to the appropriate preparation of the input data.

Słowa kluczowe

EN

biometrics; person authentication; feature selection; Hotelling’s statistic

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2022
• Tom: Vol. 12, No. 1
• Strony: 41--59
• Opis fizyczny: Bibliogr. 40 poz., rys.

Twórcy

autor

Doroz Rafal

• Institute of Computer Science, Faculty of Science and Technology, University of Silesia, ul. Bedzinska 39, 41-200 Sosnowiec, Poland

autor

Wrobel Krzysztof

• Institute of Computer Science, Faculty of Science and Technology, University of Silesia, ul. Bedzinska 39, 41-200 Sosnowiec, Poland

autor

Porwik Piotr

• Institute of Computer Science, Faculty of Science and Technology, University of Silesia, ul. Bedzinska 39, 41-200 Sosnowiec, Poland

autor

Orczyk Tomasz

• Institute of Computer Science, Faculty of Science and Technology, University of Silesia, ul. Bedzinska 39, 41-200 Sosnowiec, Poland

Bibliografia

• [1] Marcin Zalasinski, Krystian Łapa, Krzysztof Ćpałka, Krzysztof Przybyszewski, and Gary G. Yen. On-line signature partitioning using a population based algorithm. Journal of Artificial Intelligence and Soft Computing Research, 10(1):5–13, 2020.
• [2] Marcin Zalasinski, Krzysztof Cpałka, Łukasz Laskowski, Donald C. Wunsch II, and Krzysztof Przybyszewski. An algorithm for the evolutionary fuzzy generation of on-line signature hybrid descriptors. Journal of Artificial Intelligence and Soft Computing Research, 10(3):173–187, 2020.
• [3] Rafal Doroz, Krzysztof Wrobel, and Piotr Porwik. An accurate fingerprint reference point determination method based on curvature estimation of separated ridges. Int. J. Appl. Math. Comput. Sci., 28(1):209–225, 2018.
• [4] Geevar C. Zacharias, Madhu S. Nair, and P. Sojan Lal. Fingerprint reference point identification based on chain encoded discrete curvature and bending energy. Pattern Analysis and Applications, 20(1):253–267, 2017.
• [5] D. Kim, K. Chung, and K. Hong. Person authentication using face, teeth and voice modalities for mobile device security. IEEE Transactions on Consumer Electronics, 56(4):2678–2685, 2010.
• [6] Santosh Behera, Debi Dogra, and Partha Roy. Analysis of 3d signatures recorded using leap motion sensor. Multimedia Tools and Applications, 77:14029–14054, 2018.
• [7] Daniel Bachmann, Frank Weichert, and Gerhard Rinkenauer. Evaluation of the leap motion controller as a new contact-free pointing device. Sensors, 15:214–233, 2015.
• [8] Elyoenai Guerra-Segura, Aysse Ortega-Perez, and Carlos M. Travieso. In-air signature verification system using leap motion. Expert Systems with Applications, 165:113797, 2021.
• [9] Feriel Cherifi, Mawloud Omar, and Kamal Amroun. An efficient biometric-based continuous authentication scheme with hmm prehensile movements modeling. Journal of Information Security and Applications, 57:102739, 2021.
• [10] Ruoshi Wen, Qiang Wang, and Zhibin Li. Human hand movement recognition using infinite hidden markov model based semg classification. Biomedical Signal Processing and Control, 68:102592, 2021.
• [11] Gonzalo Bailador, Carmen Sanchez-Avila, Javier Guerra-Casanova, and Alberto de Santos Sierra. Analysis of pattern recognition techniques for inair signature biometrics. Pattern Recognition, 44(10–11):2468–2478, 2011.
• [12] Badreddine Ben Nouma, Amar Mitiche, Youssef Ouakrim, and Neila Mezghani. Pattern classification by the hotelling statistic and application to knee osteoarthritis kinematic signals. Machine Learning and Knowledge Extraction, 1:768–784, 07 2019.
• [13] Piotr Porwik, Rafal Doroz, and Tomasz Orczyk. The k-nn classifier and self-adaptive hotelling data reduction technique in handwritten signatures recognition. Pattern Anal. Appl., 18(4):983–1001, 2015.
• [14] Alexandros Zaharis, Adamantini Martini, Panayotis Kikiras, and George Stamoulis. User authentication method and implementation using a three-axis accelerometer. In Periklis Chatzimisios, Christos Verikoukis, Ignacio Santamar´ıa, Massimiliano Laddomada, and Oliver Hoffmann, editors, Mobile Lightweight Wireless Systems, pages 192–202, Berlin, Heidelberg, 2010. Springer Berlin Heidelberg.
• [15] Richard Haskell, Darrin Hanna, and Kevin Sickle. 3d signature biometrics using curvature moments. In Proceedings of the 2006 International Conference on Artificial Intelligence, ICAI, USA, pages 718–721, 2006.
• [16] Tomasz Grzejszczak, Reinhard Molle, and Robert Roth. Tracking of dynamic gesture fingertips position in video sequence. Archives of Control Sciences, vol. 30(No 1):101–122, 2020.
• [17] Nahumi Nugrahaningsih, Marco Porta, and Giuseppe Scarpello. A hand gesture approach to biometrics. In Vittorio Murino, Enrico Puppo, Diego Sona, Marco Cristani, and Carlo Sansone, editors, New Trends in Image Analysis and Processing – ICIAP 2015 Workshops, pages 51–58, Cham, 2015. Springer International Publishing.
• [18] Md Wasiur Rahman, Fatema Tuz Zohra, and Marina L. Gavrilova. Score level and rank level fusion for kinect-based multi-modal biometric system. Journal of Artificial Intelligence and Soft Computing Research, 9(3):167–176, 2019.
• [19] Joze Guna, Emilija Stojmenova, Artur Lugmayr, Iztok Humar, and Matevz Pogacnik. User identification approach based on simple gestures. Multimedia Tools and Applications, 71(1):179–194, 2014.
• [20] Piero Zappi, Bojan Milosevic, Elisabetta Farella, and Luca Benini. Hidden markov model based gesture recognition on low-cost, low-power tangible user interfaces. Entertainment Computing, 1(2):75–84, 2009.
• [21] Elisabetta Farella, Sile O’Modhrain, Luca Benini,and Bruno Ricco. Gesture signature for ambient intelligence applications: A feasibility study. In Kenneth P. Fishkin, Bernt Schiele, Paddy Nixon, and Aaron Quigley, editors, Pervasive Computing, pages 288–304, Berlin, Heidelberg, 2006. Springer Berlin Heidelberg.
• [22] Eduardo S. Silva, Jader Abreu, Janiel Henrique Pinheiro de Almeida, Veronica Teichrieb, and Geber Lisboa Ramalho. A preliminary evaluation of the leap motion sensor as controller of new digital musical instruments. In Proceedings of SBCM -Brazilian Symposium on Computer Music. Brasil., 2013.
• [23] Abdul Butt, Erika Rovini, Cristina Dolciotti, Paolo Bongioanni, Gianluca De Petris, and Filippo Cavallo. Leap motion evaluation for assessment of upper limb motor skills in parkinson’s disease. In 2017 International Conference on Rehabilitation Robotics (ICORR), pages 116–121, 2017.
• [24] Roland Partridge, Fraser Brown, Paul Brennan, Iain Hennessey, and Mark Hughes. The leaptm gesture interface device and take-home laparoscopic simulators: A study of construct and concurrent validity. Surgical innovation, 23:70–77, 2015.
• [25] Pradeep Kumar, Himaanshu Gauba, Partha Pratim Roy, and Debi Prosad Dogra. Coupled hmm-based multi-sensor data fusion for sign language recognition. Pattern Recognition Letters, 86:1–8, 2017.
• [26] A. Chahar, S. Yadav, I. Nigam, R. Singh, and M. Vatsa. A leap password based verification system. In 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), pages 1–6, 2015.
• [27] Ana M. Bernardos, Jose M. Sanchez, Javier I. Portillo, Xian Wang, Juan A. Besada, and Jose R. Casar. Design and deployment of a contactless hand-shape identification system for smart spaces. J. Ambient Intell. Humaniz. Comput., 7(3):357–370, 2016.
• [28] Alexander Chan, Tzipora Halevi, and Nasir D. Memon. Leap motion controller for authentication via hand geometry and gestures. In Theo Tryfonas and Ioannis G. Askoxylakis, editors, Human Aspects of Information Security, Privacy, and Trust -Third International Conference, HAS 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015. Proceedings, volume 9190 of Lecture Notes in Computer Science, pages 13–22. Springer, 2015.
• [29] Elyoenai Guerra-Segura, Carlos M. Travieso, and Jesus B. Alonso. Study of the variability of the leap motion’s measures for its use to characterize air strokes. Measurement, 105:87–97, 2017.
• [30] Piotr Porwik, Rafal Doroz, and Tomasz Orczyk. Signatures verification based on pnn classifier optimised by pso algorithm. Pattern Recognition, 60:998 – 1014, 2016.
• [31] Krzysztof Wrobel. Diagnosing parkinson’s disease with the use of a reduced set of patients’ voice features samples. In Khalid Saeed, Rituparna Chaki, and Valentina Janev, editors, Computer Information Systems and Industrial Management, pages 84–95, Cham, 2019. Springer International Publishing.
• [32] Toshiro Tango. 100 statistical tests. gopal k. kanji, sage publications, london, 1999. Statistics in Medicine, 19(21):3018–3018, 2000.
• [33] P. D. T. O’connor. Statistical methods for quality improvement, t. p. ryan, wiley interscience, 1989.Quality and Reliability Engineering International, 5(4):339–339, 1989.
• [34] Dale G. Sauers. Hotelling’s t2 statistic for multivariate statistical process control: A nonrigorous approach. Quality Engineering, 9(4):627–634, 1997.
• [35] Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal. Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques. organ Kaufmann Publishers Inc., San Francisco, CA, USA, 4th edition, 2016.
• [36] Alessio Benavoli, Giorgio Corani, Janez Demsar, and Marco Zaffalon. Time for a change: a tutorial for comparing multiple classifiers through bayesian analysis. Journal of Machine Learning Research, 18(77):1–36, 2017.
• [37] Ilhan Aslan, Andreas Uhl, Alexander Meschtscherjakov, and Manfred Tscheligi. Design and exploration of mid-air authentication gestures. ACM Transactions on Interactive Intelligent Systems, 6(3), 2016.
• [38] Ana M. Bernardos, Jose M. Sanchez, Javier I. Portillo, Juan A. Besada, and Jose R. Casar. A contact- less identification system based on hand shape features. Procedia Computer Science, 52:161 – 168, 2015. The 6th International Conference on Ambient Systems, Networks and Technologies (ANT2015), the 5th International Conference on Sustainable Energy Information Technology (SEIT-2015).
• [39] Chetna Naidu and Archana Ghotkar. Hand gesture recognition using leap motion controller. International Journal of Science and Research, 5(10):436 – 441, 2016.
• [40] Santosh Kumar Behera, Ajaya Kumar Dash, D. P. Dogra, and P. Roy. Air signature recognition using deep convolutional neural network-based sequential model. 2018 24th International Conference on Pattern Recognition (ICPR), pages 3525–3530, 2018.