Person recognition based on touch screen gestures using computational intelligence methods

Rzecki, Krzysztof; Pławiak, Paweł; Niedźwiecki, Michał; Sośnicki, Tomasz; Leśkow, Jacek; Ciesielski, Maciej

doi:10.1016/j.ins.2017.05.041

Cited by 42 publications

(13 citation statements)

References 17 publications

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“…Biometric identification systems are widely used in security-critical systems [1][2][3] and man-machine interfaces (MMI) [4]. They are used for personnel control and criminal monitoring/detection in areas such as military, hospital, airport, education [3,[5][6][7]. In systems used for personnel control, persons are registered, and recognition is performed with the perception of biometric data with certain standards.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

et al. 2019

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In this study, a new face recognition architecture is proposed using fuzzy-based Discrete Wavelet Transform (DWT) and fuzzy with two novel local graph descriptors. These graph descriptors are called Local Cross Pattern (LCP). The proposed fuzzy wavelet-based face recognition architecture consists of DWT, Triangular fuzzy set transformation, and textural feature extraction with local descriptors and classification phases. Firstly, the LL (Low-Low) sub-band is obtained by applying the 2 Dimensions Discrete Wavelet Transform (2D DWT) to face images. After that, the triangular fuzzy transformation is applied to this band in order to obtain A, B, and C images. The proposed LCP is then applied to the B image. LCP consists of two types of descriptors: Vertical Local Cross Pattern (VLCP) and Horizontal Local Cross Pattern (HLCP). Linear discriminant analysis, quadratic discriminant, analysis, quadratic kernel-based support vector machine (QKSVM), and K-nearest neighbors (KNN) were ultimately used to classify the extracted features. Ten widely used descriptors in the literature are applied to the fuzzy wavelet architecture. AT&T, CIE, Face94, and FERET databases are used for performance evaluation of the proposed methods. Experimental results show that the LCP descriptors have high face recognition performance, and the fuzzy wavelet-based model significantly improves the performances of the textural descriptors-based face recognition methods. Moreover, the proposed fuzzy-based domain and LCP method achieved classification accuracy rates of 97.3%, 100.0%, 100.0%, and 96.3% for AT&T, CIE, Face94, and FERET datasets, respectively.

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Section: Introductionmentioning

confidence: 99%

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

et al. 2019

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“…In this scenario, if the remote is broken or lost, the robot loses control and leads to hazards and is waste of money as well. To overcome this remote-control concept, controlling the robot using a gesture recognition technique with an accelerometer [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33], Bluetooth android application [34,35,36,37,38,39,40,41], and voice recognition [42,43,44,45,46,47,48,49,50] have been proposed. Thus, in previous literature, most of them are line-sensing robots [51,52,53] that are controlled with Infrared LEDs, HF band short-range RFID systems [54], and CNY 70 sensors [55].…”

Section: Introductionmentioning

confidence: 99%

Single-Equipment with Multiple-Application for an Automated Robot-Car Control System

Ullah,

Mumtaz,

Liu

et al. 2019

Sensors

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The integration of greater functionalities into vehicles increases the complexity of car-controlling. Many research efforts are dedicated to designing car-controlling systems that allow users to instruct the car just to show it what it should do; however, for non-expert users, controlling the car with a remote or a switch is complicated. So, keeping this in mind, this paper presents an Arduino based car-controlling system that no longer requires manual control of the cars. Two main contributions are presented in this work. Firstly, we show that the car can be controlled with hand-gestures, according to the movement and position of the hand. The hand-gesture system works with an Arduino Nano, accelerometer, and radio-frequency (RF) transmitter. The accelerometer (attached with the hand-glove) senses the acceleration forces that are produced by the hand movement, and it will transfer the data to the Arduino Nano that is placed on hand glove. After receiving the data, Arduino Nano will convert it into different angle values in ranges of 0°–450° and send the data to the RF receiver of the Arduino Uno, which is placed on the car through the RF transmitter. Secondly, the proposed car system is to be controlled by an android based mobile-application with different modes (e.g., touch buttons mode, voice recognition mode). The mobile-application system is the extension of the hand-gesture system with the addition of Bluetooth module. In this case, whenever the user presses any of the touch buttons in the application, and/or gives voice commands, the corresponding signal is sent to the Arduino Uno. After receiving the signal, Arduino will check this against its predefined instructions for moving forward, backward, left, right, and brake; then it will send the command to the motor module to move the car in the corresponding direction. In addition, an automatic obstacle detection system is introduced to improve the safety measurements to avoid any hazards with the help of sensors placed at the front of the car. The proposed systems are designed as a lab-scale prototype to experimentally validate the efficiency, accuracy, and affordability of the systems. The experimental results prove that the proposed work has all in one capability (hand-gesture, touch buttons and voice-recognition with mobile-application, obstacle detection), is very easy to use, and can be easily assembled in a simple hardware circuit. We remark that the proposed systems can be implemented under real conditions at large-scale in the future, which will be useful in automobiles and robotics applications.

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“…However, the same could be used to develop a convolutional neural network for the detection of any other object. Using computational intelligence methods, such as probabilistic neural network, radial basis function network and multi-layer perceptron, Rzecki (2017, p. 71) [7] designed and built a data acquisition system to collect surveys resulting from the execution of single finger gestures on a mobile device screen, to propose a data pre-processing procedure and to indicate the best classification method for person recognition based on these surveys. They acquired and analyzed gestures from fifty persons, each performing nine gestures for ten repetitions (4500 surveys).…”

Section: Introductionmentioning

confidence: 99%

Sustainability through the Use of Modern Simulation Methods—Applied Artificial Intelligence

et al. 2019

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As the transformative power of AI crosses all economic and social sectors, the use of it as a modern technique for the simulation and/or forecast of various indicators must be viewed as a tool for sustainable development. The present paper reveals the results of research on modeling and simulating the influences of four economic indicators (the production in industry, the intramural research and development expenditure, the turnover and volume of sales and employment) on the evolution of European Economic Sentiment using artificial intelligence. The main goal of the research was to build, train and validate an artificial neural network that is able to forecast the following year’s value of economic sentiment using the present values of the other indicators. Research on predicting European Economic Sentiment Indicator (ESI) using artificial neural networks is a starting point, with work on this subject almost inexistent, the reason being mainly that ESI is a composite of five sectoral confidence indicators and is not thought to be an emotional response to the interaction of the entrepreneurial population with different economic indicators. The authors investigated, without involving a direct mathematical interaction among the indicators involved, predicting ESI based on a cognitive response. Considering the aim of the research, the method used was simulation with an artificial neural network and a feedforward network (structure 4-9-6-1) and a backward propagation instruction algorithm was built. The data used are euro area values (for 19 countries only—EA19) recorded between 1999 and 2016, with Eurostat as the European Commission’s statistical data website. To validate the results, the authors imposed the following targets: the result of the neural network training error is less than 5% and the prediction verification error is less than 10%. The research outcomes resulted in a training error (after 30,878 iterations) of less than 0.099% and a predictive check error of 2.02%, which resulted in the conclusion of accurate training and an efficient prediction. AI and artificial neural networks, are modeling and simulation methods that can yield results of nonlinear problems that cover, for example, human decisions based on human cognitive processes as a result of previous experiences. ANN copies the structure and functioning of the biological brain, having the advantage through learning and coaching processes (biological cognitive), to copy/predict the results of the thinking process and, thus, the process of choice by the biological brain. The importance of the present paper and its results stems from the authors’ desire to use and popularize modern methods of predicting the different macroeconomic indices that influence the behavior of entrepreneurs and therefore the decisions of these entrepreneurs based on cognitive response more than considering linear mathematical functions that cannot correctly understand and anticipate financial crises or economic convulsions. Using methods such as AI, we can anticipate micro- and macroeconomic developments, and therefore react in the direction of diminishing their negative effects for companies as well as the national economy or European economy.

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Person recognition based on touch screen gestures using computational intelligence methods

Cited by 42 publications

References 17 publications

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

Single-Equipment with Multiple-Application for an Automated Robot-Car Control System

Sustainability through the Use of Modern Simulation Methods—Applied Artificial Intelligence

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