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Analysis of typing pattern in identifying soft biometric information and its impact in user recognition

Analysis of typing pattern in identifying soft biometric inf...

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international conference on Information Technology and Applied Mathematics, ICITAM 2017

作者： Roy, Soumen Roy, Utpal Sinha, D.D. University of Calcutta KolkataWB700009 India Visva-Bharati SantiniketanWB731235 India

ISBN: (纸本)9789811075896

As of now, the performance of keystroke dynamics biometric in user recognition is not acceptable in practice due to intra-class variations, high failure to enroll rate (FER) or various troubles in data acquisition methods or diverse use of sensing devices. As per the previous study, the performance of this technique can be improved by incorporation of gender information, a soft biometric characteristic, extracted from the typing pattern on a computer keyboard that provides some additional information about the user. This soft biometric trait has low user discriminating power but can be used to enhance the performance of user recognition in accuracy and time efficiency. Furthermore, it has been observed that the age group (18–30/30+ or © 2019, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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4th international conference on Research in Intelligent and computing in Engineering, RICE 2019

4th International Conference on Research in Intelligent and ...

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4th international conference on Research in Intelligent and computing in Engineering, RICE 2019

ISBN: (纸本)9789811527791

The proceedings contain 123 papers. The special focus in this conference is on Research in Intelligent and computing in Engineering. The topics include: Concept of cancer treatment by heating methodology of microwave;dynamics of self-guided rocket control with the optimal angle coordinate system combined with measuring target parameters for frequency modulated continuous wave radar;an approach of utilizing binary bat algorithm for pattern nulling;An application of WSN in smart aquaculture farming;a newly developed approach for transmit beamforming in multicast transmission;post-quantum commutative deniable encryption algorithm;Indoor positioning using BLE iBeacon, smartphone sensors, and distance-based position correction algorithm;assessing the transient structure with respect to the voltage stability in large power system;cellular automata approach for optimizing radio coverage: A case study on archipelago surveillance;smart bicycle: IoT-based transportation service;LNA nonlinear distortion impacts in multichannel direct RF digitization receivers and linearization techniques;novel approach to detect and extract the contents in a picture or image;Modified biological model of meat in the frequency range from 50 Hz to 1 MHz;A research on clustering and identifying automated communication in the HTTP environment;Sequential all-digital background calibration for channel mismatches in time-interleaved ADC;Comparison BICM-ID to turbo code in wide band communication systems in the future;a design of a vestibular disorder evaluation system;about model separation techniques and control problems of wheeled mobile robots;Fuzzy supply chain performance measurement model based on SCOR 12.0;lightweight convolution neural network based on feature concatenate for facial expression recognition;light fidelity system.

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Facial Analysis Using Jacobians and Gradient Boosting

Facial Analysis Using Jacobians and Gradient Boosting

引用

international conference on Mathematical Modelling and Scientific computing

作者： Vinay, A. Gupta, Abhijay Kamath, Vinayaka R. Bharadwaj, Aprameya Srinivas, Arvind Murthy, K. N. B. Natarajan, S. PES Univ Ctr Pattern Recognit & Machine Intelligence Bengaluru India

ISBN: (纸本)9789811513381;9789811513374

Security and identity have become one of the primary concerns of the people in this digital world. Person authentication and identification is transforming the way these services are provided. Earlier it was mainly achieved through passwords and patterns but with significant advancements in face recognition technologies, it has been exploited in providing authentication in smart phones and computers. Face recognition (FR) extends its use in applications like face tagging in social media, surveillance system at theaters, airports and so on. The proposed mathematical model employs linear algebra and mathematical simulations for the task of person identification. Kernel singular value decomposition is used to denoise the facial image which is then passed to a feature detector and descriptor based on nonlinear diffusion filtering. The obtained descriptors are quantized into a vector using an encoding model called VLAD which uses k-means++ for clustering. Further, classification is done using Gradient boosting decision trees. The pipeline proposed aims at reducing the average computational power and also enhancing the efficiency of the system. The proposed system has been tested on the three benchmark datasets namely Face 95, Face 96 and Grimace.

关键词： Linear algebra Kernel-SVD Feature quantization Gradient boosted decision tree

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Towards efficient and real-time human activity recognition using wearable sensors: A shapelet-based pattern matching approach 13th

Towards efficient and real-time human activity recognition u...

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13th EAI international conference on Body Area Networks, BODYNETS 2018

作者： Yazdansepas, Delaram Saroha, Nitin Ramaswamy, Lakshmish Rasheed, Khaled Department of Computer Science University of Georgia AthensGA United States

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Facial Image Classification Using Rotation, Illumination, Scale and Expression Invariant Dense Features and ENN

Facial Image Classification Using Rotation, Illumination, Sc...

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international conference on Mathematical Modelling and Scientific computing

作者： Vinay, A. Singh, Ankur Anand, Nikhil Raj, Mayank Bharati, Aniket Murthy, K. N. B. Natarajan, S. PES Univ Ctr Pattern Recognit & Machine Intelligence 100 Feet Ring RdBanashankari Stage 3 Bengaluru 560085 Karnataka India

ISBN: (纸本)9789811513381;9789811513374

Face recognition is immensely proliferating as a research area in the paradigm of Computer Vision as it provides an extensive choice of applications in surveillance and commercial domains. This paper throws light upon the comparison of various dense feature descriptors (Dense SURF, Dense SIFT, Dense ORB) with each other and also with their classical counterparts (SURF, SIFT, ORB) using a novel technique for recognition. This proposed technique uses Laplacian of Gaussian filter for enhancement of the image. It applies various dense and classical feature descriptors on the enhanced image and outputs a feature vector. In order to achieve high performance, this feature vector is given to Fisher vector since Fisher Vector is a feature patch-aggregation method. Finally, extended nearest neighbor Classifier is used for classification over the orthodox k-nearest classifier. Experiments were carried out on three diverse datasets-ORL, Faces94, and Grimace. On scrutinizing the results, Dense SIFT and Dense ORB were found to be preeminent as measured by various performance metrics. 98.44 on Grimace, 98.15 on Faces94.

关键词： Scale invariant feature transformation Speed up robust feature Oriented FAST and rotated BRIEF Extended nearest neighbor Laplacian of Gaussian

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Ensemble Forecasting of Monthly Electricity Demand Using pattern Similarity-Based Methods 19th

Ensemble Forecasting of Monthly Electricity Demand Using Pat...

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19th international conference on Artificial Intelligence and soft computing, ICAISC 2020

作者： Pelka, Pawel Dudek, Grzegorz Electrical Engineering Faculty Częstochowa University of Technology Częstochowa Poland

ISBN: (纸本)9783030614003

This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs). PSFMs applied in this study include k-nearest neighbor model, fuzzy neighborhood model, kernel regression model, and general regression neural network. An integral part of PSFMs is a time series representation using patterns of time series sequences. pattern representation ensures the input and output data unification through filtering a trend and equalizing variance. Two types of ensembles are created: heterogeneous and homogeneous. The former consists of different type base models, while the latter consists of a single-type base model. Five strategies are used for controlling a diversity of members in a homogeneous approach. The diversity is generated using different subsets of training data, different subsets of features, randomly disrupted input and output variables, and randomly disrupted model parameters. An empirical illustration applies the ensemble models as well as individual PSFMs for comparison to the monthly electricity demand forecasting for 35 European countries. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Application of Elastic Principal Component Analysis to Person recognition Based on Screen Gestures 18th

Application of Elastic Principal Component Analysis to Perso...

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18th international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Baran, Mateusz Siwik, Leszek Rzecki, Krzysztof Cracow Univ Technol Fac Phys Math & Comp Sci Ul Warszawska 24 PL-31155 Krakow Poland AGH Univ Sci & Technol Dept Comp Sci Krakow Poland

ISBN: (纸本)9783030209124;9783030209117

Person identification based on touch screen gestures is a well-known method of authentication in mobile devices. Usually it is only checked if the user entered the correct pattern. Taking into account other biometric data based on the speed and shape of finger movements can provide higher security while the convenience of this authorisation method is not impacted. In this work the application of Sequential Joint Functional Principal Analysis (FPCA) as a dimensionality reduction method for gesture data is explored. Performance of the classifier is measured using 5-fold stratified cross-validation on a set of gestures collected from 12 people. The effects of sampling rate on classification performance is also measured. It is shown that the Support Vector Machine classifier reaches the accuracy of 79% using features obtained using the Sequential Joint FPCA, compared to 70% in the case of Euclidean PCA.

关键词： Touch screen gestures Biometrics Classification Elastic shape analysis pattern recognition

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2nd international conference on Innovative computing and Communication, ICICC 2019

2nd International Conference on Innovative Computing and Com...

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2nd international conference on Innovative computing and Communication, ICICC 2019

ISBN: (纸本)9789811503238

The proceedings contain 50 papers. The special focus in this conference is on Innovative computing and Communication. The topics include: Comparative Study of TDMA-Based MAC Protocols in VANET: A Mirror Review;node Authentication in IoT-Enabled Sensor Network Using Middleware;an Efficient Data Aggregation Approach for Prolonging Lifetime of Wireless Sensor Network;a Comprehensive Review of Keystroke Dynamics-Based Authentication Mechanism;performance Analysis of Routing Protocols in Wireless Sensor Networks;Controlled Access Energy Coding (CAEC) for Wireless Adhoc Network;ioT-Based HelpAgeSensor Device for Senior Citizens;Optimization of LEACH for Developing Effective Energy-Efficient Protocol in WSN;automated Vehicle Management System Using Wireless Technology;a Supply Chain Replenishment Inflationary Inventory Model with Trade Credit;experimental Analysis of OpenStack Effect on Host Resources Utilization;customer Churn Prediction in Telecommunications Using Gradient Boosted Trees;efficient Evolutionary Approach for Virtual Machine Placement in Cloud Data Center;SCiJP: Solving computing Issues by Java Design pattern;permissioned Blockchain-Based Agriculture Network in Rootnet Protocol;load Balancing and Fault Tolerance-Based Routing in Wireless Sensor Networks;a Novel Context Migration Model for Fog-Enabled Cross-Vertical IoT Applications;equity Data Distribution Algorithms on Identical Routers;improved Leakage Current Performance in Domino Logic Using Negative Differential Resistance Keeper;an Efficient Parking Solution for Shopping Malls Using Hybrid Fog Architecture;Sensor’s Energy and Performance Enhancement Using LIBP in Contiki with Cooja;effect of Dropout and Batch Normalization in Siamese Network for Face recognition;Analysis and Mitigation of DDoS Flooding Attacks in software Defined Networks;Analysis of Impact of Network Topologies on Network Performance in SDN.

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Remarks on recognition of Aromas from Tea Leaves Using Deep Neural Network based on PPF-coated QCR Sensor Signals 3

Remarks on Recognition of Aromas from Tea Leaves Using Deep ...

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3rd IEEE international conference on Robotic computing (IRC)

作者： Takahashi, Kazuhiko Sugimoto, Iwao Doshisha Univ Fac Sci & Engn Kyoto 6100321 Japan Tokyo Univ Technol Sch Comp Sci Tokyo 1920982 Japan

ISBN: (纸本)9781538692455

This study investigated the capacity of a deep neural network to distinguish tea types based on their aromas. The data set of aromas from tea leaves, which contained sensor responses measured with a gas-sensing system using a mass-sensitive chemical sensor comprised of an array of quartz crystal resonators coated with plasma organic polymer films, was used to evaluate the recognition accuracy. To define the input vectors of the deep neural network in aroma recognition experiments, frequency analysis using a continuous wavelet transform, with the Morlet function as the mother wavelet, was used to extract features from the sensor signals of the data set. The deep neural network achieved a recognition accuracy of 100% for the three tea types: oolong, jasmine and pu'erh, and the base gas of dehumidified indoor air. Comparing the recognition accuracy of the deep neural network to that obtained from other pattern recognition methods, such as naive Bayes and random forests, the experimental results demonstrated the effectiveness of applying a deep neural network to this task.

关键词： Neural networks Feature extraction Robot sensing systems Continuous wavelet transforms pattern recognition

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The Extreme Learning Machine Algorithm for Classifying Fingerprints 39

The Extreme Learning Machine Algorithm for Classifying Finge...

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39th international conference of the Chilean-Computer-Science-Society (SCCC)

作者： Zabala-Blanco, David Mora, Marco Hernandez-Garcia, Ruber Barrientos, Ricardo J. Univ Catolica Maule Dept Comp Sci & Ind Talca 3480112 Chile Univ Catolica Maule Res Ctr Adv Studies Maule CIEAM Talca 3480112 Chile Univ Catolica Maule Lab Technol Res Pattern Recognit LITRP Talca 3480112 Chile

ISBN: (纸本)9781728183282

Fingerprint recognition is the most employed biometric method for identification and verification purposes. Fingerprint images are classified into five categories according to the morphology of their ridges, which decreases the database penetration rate on an identification scheme. The classification procedure mainly starts with the feature extraction from the fingerprint sample, based on minutiae obtained from terminations and bifurcations of ridges. Afterward, the classification process is usually carried out by some artificial neural networks under supervised learning. Recently, convolutional neural networks are utilized as a potential alternative, by showing accuracies close to 100% with a high cost of learning times even using high-performance computing. On the other hand, the extreme learning machine (ELM) has emerged as a novel algorithm for the single-hidden layer feed-forward neural network, because of its good generalization performance at extremely fast learning speed. In this work, we introduce the ELMs for the fingerprint classification problem. The superior ELMs are given by the mapping activation function and the number of hidden nodes that maximize the accuracy of the classification;a heuristic approach is carried out to find these parameters. The studied databases are the NIST-4 and SFINGE, which are composed by different quantity and quality of fingerprint samples. Results show that ELM classification by using the feature descriptor of Hong08 achieves very high accuracy and low penetration rate, reducing severally the training time in comparison with deep learning approaches.

关键词： Extreme learning machine Fingerprint classification Supervised learning

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