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CAS-AIR-3D Face: A Low-Quality, Multi-Modal and Multi-Pose 3D Face Database

CAS-AIR-3D Face: A Low-Quality, Multi-Modal and Multi-Pose 3...

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international Joint conference on Biometrics (IJCB)

作者： Li, Qi Dong, Xiaoxiao Wang, Weining Shan, Caifeng CASIA Ctr Res Intelligent Percept & Comp Beijing Peoples R China CASIA Natl Lab Pattern Recognit Beijing Peoples R China Chinese Acad Sci Artificial Intelligence Res Qingdao Peoples R China Shandong Univ Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9781665437806

Benefiting from deep learning with large scale face databases, 2D face recognition has made significant progress in recent years. However, it still highly depends on lighting conditions and human poses, and suffers from face spoofing problem. In contrast, 3D face recognition reveals a new path that can overcome the previous limitations of 2D face recognition. One of the most important problems for 3D face recognition is to construct a suitable database, which can be exploited to train different 3D face recognition algorithms. In this work, we propose a new database, CAS-AIR-3D Face, for low-quality 3D face recognition. It includes 24713 videos from 3093 individuals, which is captured by Intel RealSense SR305. The database contains three modalities: color, depth and near infrared, and is rich in pose, expression, occlusion and distance variations. To the best of our konwledge, CAS-AIR-3D Face is the largest low-quality 3D face database in terms of the number of individuals and the sample variations. Moreover, we preprocess the data via a sophisticated face alignment method, and Point Cloud Spherical Cropping Method (SCM) is lever-aged to remove the background noise in the depth images. Finally, an evaluation protocol is designed for fair comparison, and extensive experiments are conducted with different backbone networks to provide different baselines on this database.

关键词： Deep learning Three-dimensional displays Protocols Databases Image color analysis Face recognition conferences

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analysis of t-distribution in Variable Step Size Firefly Algorithm in the Applications of Machine Learning 1

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2nd international conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2020

作者： Sarangi, Shubhendu Kumar Sarangi, Archana Department of Electronics and Instrumentation Engineering ITER Siksha ‘O’ Anusandhan deemed to be University Bhubaneswar Odisha India Department of Computer Science and Engineering ITER Siksha ‘O’ Anusandhan deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9789811587528

ISBN: (纸本)9789811587511

The firefly algorithm in the field of swarm intelligence is recognized as one of the widely demanded algorithm for machine learning applications. This algorithm is inspired by natural leaving pattern of firefly. This algorithm is modified by several researchers for getting better exploration of solution space for various applications. In all these modifications the variable step size firefly algorithm is gathering popularity in the field of machine learning because of its simplicity in modification. In this paper, this modified version is further enhanced by the addition of t-distribution function. This newly proposed version helps in the improvement of exploration along with the exploitation of the searched space to generate better solutions. Simulation of the novel projected version is done with standard benchmarking functions to prove the enhancement in the solution. The analysis of results proves the betterment of the solution in a variety of cases. This approach of modification can be utilized for applications in machine learning. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Distribution functions

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Utilization of IMU-Based Gesture Recognition in the Treatment of Diabetes

Utilization of IMU-Based Gesture Recognition in the Treatmen...

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IEEE international conference on Automation, Quality and Testing, robotics, AQTR

作者： Marcell Szá ntó Gergő Strasser Lá szló Szá sz Lehel Dé nes-Fazakas Gyö rgy Eigner Gá bor Kerté sz Levente Ková cs Physiological Controls Research Center &#x00D3 buda University Budap&#x00E9 st Software Engineering Institute John von Neumann Faculty of Informatics &#x00D3 buda University Budapest Biomatics and Applied Artificial Intelligence Institute John von Neumann Faculty of Informatics &#x00D3 Applied Informatics and Applied Mathematics Doctoral School &#x00D3 E&#x00F6 tv&#x00F6 s L&#x00F3 r&#x00E1 nd Research Network Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9781665479332

ISBN: (纸本)9781665479349

Changes in blood sugar levels are significantly affected by dietary carbohydrate intake, which clearly affects insulin therapy. However, currently available insulin pumps do not perform continuous blood glucose monitoring, and the patient should do so. The quality of the treatment can be further improved if the phenomenon of eating can be recognized based on the characteristic movements, and then by sending an automatic signal of the event to the insulin pump, it can be taken into account that an increase in blood sugar levels is expected. In our work, we focused on this task and, using 4 different tools, collected data from more than 8 people on their hand movements during their meals, on which we performed various data manipulations. Finally, using the aggregation of the obtained data set, we tested different machine learning techniques, from which we will select the optimal algorithm that can recognize the motion characteristic of carbohydrate intake independently of the person with the smallest error. The article presents the tools used for the measurement, the operations performed on the data, as well as the tried and tested machine learning methods and their comparative analysis. Our goal is to improve the quality of life of people with diabetes by increasing the effectiveness of base bolus therapy.

关键词： Medical treatment Insulin pumps Gesture recognition Data collection Diabetes Character recognition Biosensors

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Performance analysis of Complex Manufacturing System using Petri Nets Modeling Method 1

Performance Analysis of Complex Manufacturing System using P...

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1st international conference on Mechatronics and Artificial intelligence, ICMAI 2021

作者： Kumar, Amit Kumar, Vinod Modgil, Vikas Kumar, Ajay Sharma, Anita Department of Mechanical Engineering Shree Guru Gobind Singh Tricentenary University Budhera Haryana Gurugram122505 India Department of Mechanical Engineering National Institute of Technology Haryana Kurukshetra136119 India Department of Mechanical Engineering Deenbandhu Chhotu Ram University of Science and Technology Murthal Haryana Sonipat131027 India School of Management K.R. Mangalam University Haryana Gurugram122103 India

The Petri nets modeling method is a powerful tool for the performance analysis of industrial systems. The main factor which makes it more effective among the various popular simulation methods is the ability to deal with the real working conditions. The data obtained from such a manufacturing system is generally full of uncertainties and the Petri nets modeling method deals with this data to reflect the real behavioral pattern of different sub-systems installed in the plant. The Petri nets-based simulation method provides the availability of the sub-systems for a long time period by running the plant in virtual manners. The results obtained through the analysis can be utilized to identify those sub-systems which highly affect the system availability and separate maintenance planning for each sub-system can reduce the production loss due to unavailability of any sub-system for performing its intended task. The proposed methodology has been demonstrated in this paper using a complex repairable manufacturing system. © Published under licence by IOP Publishing Ltd.

关键词： Petri nets

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Skeleton-Based Action Recognition with Joint Coordinates as Feature Using Neural Oblivious Decision Ensembles 20

Skeleton-Based Action Recognition with Joint Coordinates as ...

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20th international conference on New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT)

作者： Nasrul'Alam, Fakhrul Aniq Hakimi Shapiai, Mohd Ibrahim Batool, Uzma Ramli, Ahmad Kamal Elias, Khairil Ashraf Univ Teknol Malaysia Ctr Artificial Intelligence & Robot iKohza Malaysia Japan Int Inst Technol Kuala Lumpur Malaysia Univ Wah Dept Comp Sci Wah Cantt Pakistan Univ Teknol MARA Pahang Fac Comp & Math Sci Bandar Tun Razak Malaysia Selangor Int Islamic Univ Coll Fac Informat Sci & Technol Kajang Malaysia

ISBN: (纸本)9781643681955;9781643681948

Recognition of human behavior is critical in video monitoring, humancomputer interaction, video comprehension, and virtual reality. The key problem with behaviour recognition in video surveillance is the high degree of variation between and within subjects. Numerous studies have suggested background-insensitive skeleton-based as the proven detection technique. The present state-of-the-art approaches to skeleton-based action recognition rely primarily on Recurrent Neural Networks (RNN) and Convolution Neural Networks (CNN). Both methods take dynamic human skeleton as the input to the network. We chose to handle skeleton data differently, relying solely on its skeleton joint coordinates as the input. The skeleton joints' positions are defined in (x, y) coordinates. In this paper, we investigated the incorporation of the Neural Oblivious Decision Ensemble (NODE) into our proposed action classifier network. The skeleton is extracted using a pose estimation technique based on the Residual Network (ResNet). It extracts the 2D skeleton of 18 joints for each detected body. The joint coordinates of the skeleton are stored in a table in the form of rows and columns. Each row represents the position of the joints. The structured data are fed into NODE for label prediction. With the proposed network, we obtain 97.5% accuracy on RealWorld (HAR) dataset. Experimental results show that the proposed network outperforms one the state-of-the-art approaches by 1.3%. In conclusion, NODE is a promising deep learning technique for structured data analysis as compared to its machine learning counterparts such as the GBDT packages;Catboost, and XGBoost.

关键词： behavior recognition pose estimation skeleton joint coordinates structured data Neural Oblivious Decision Ensemble (NODE)

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Basic Models of Descriptive Image analysis 25th

Basic Models of Descriptive Image Analysis

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25th international conference on pattern Recognition Workshops, ICPR 2020

作者： Gurevich, Igor Yashina, Vera Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences 44/2 Vavilov Street Moscow119333 Russia

ISBN: (纸本)9783030688202

This paper is devoted to the basic models of descriptive image analysis, which is the leading branch of the modern mathematical theory of image analysis and recognition. Descriptive analysis provides for the implementation of image analysis processes in the image formalization space, the elements of which are various forms (states, phases) of the image representation that is transformed from the original form into a form that is convenient for recognition (i.e., into a model), and models for converting data representations. Image analysis processes are considered as sequences of transformations that are implemented in the phase space and provide the construction of phase states of the image, which form a phase trajectory of the image translation from the original view to the model. Two types of image analysis models are considered: 1) models that reflect the general properties of the process of image recognition and analysis – the setting of the task, the mathematical and heuristic methods used, and the algorithmic content of the process: a) a model based on a reverse algebraic closure;b) a model based on the equivalence property of images;c) a model based on multiple image models and multiple classifiers;2) models that characterize the architecture and structure of the recognition process: a) a multilevel model for combining algorithms and source data in image recognition;b) an information structure for generating descriptive algorithmic schemes for image recognition. A brief description, a comparative analysis of the relationships and specifics of these models are given. Directions for further research are discussed. © 2021, Springer Nature Switzerland AG.

关键词： Image analysis

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A Novel Safety Evaluation Approach of Transfer Interaction based on Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals

A Novel Safety Evaluation Approach of Transfer Interaction b...

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IEEE international conference on intelligence and Safety for robotics (ISR)

作者： Donghui Zhao Tianqi Zhang Junyou Yang Shuoyu Wang Hiroshi Yokoi School of Electrical Engineering Shenyang University of Technology Shenyang University of Technology Kochi University of Technology Kochi CO Japan University of Electro-Communications Tokyo CO Japan

Monitoring human-robot interaction (HRI) process of people with mobility inconvenience when to employ multi-robots is an effective approach to mitigate human error and enhance safety. However, the interaction process of transfer that occurs frequently in daily life has not been studied. In this study, we proposed a comprehensive transfer monitoring method according to the mental states monitoring by functional Near-InfraRed Spectroscopy (fNIRS) and subjective workload questionnaire. 7 subjects were to perform transfer behavior under two contrasted levels (self-rising transfer vs. assisted-rising transfer by welfare-robots). After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness, and kurtosis-were calculated. All possible 2-and 3-feature combinations of the calculated features were then used to classify self-rising transfer vs. assisted-rising transfer by linear discriminant analysis (LDA). Then, we established the Subj ective Workload Assessment System (SWAS) for 2 subj ects, which was used to make an evaluation for HRI. The experiment results demonstrated that the optimal feature-combination selection by mean and peak values, which provided a sound theoretical basis for distinguishing mental states of transfer tasks. The SWAS effectively quantify the mental states and physical load of users when to implement multiply welfare-robots. It was contributed to guide the motion planning of multiply welfare-robots for the ultimate goal of safe transfer, and also could be applied in similar scenarios.

关键词： Spectroscopy Human-robot interaction Physiology Safety Planning Linear discriminant analysis Task analysis

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pattern Matching: A Quantum Oriented Approach

Pattern Matching: A Quantum Oriented Approach

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international conference on Computational intelligence and Data Science (ICCIDS)

作者： Soni, Kapil Kumar Rasool, Akhtar MANIT Bhopal Dept Comp Sci & Engn Bhopal 462003 India

This article influences the quantum based solutions for traditional pattern matching problem, as the quantum machines can accelerate the computation speed by inherently supporting the parallel executions. The problem identifies solution by suggesting effective algorithms, and as per the classical machines several algorithms are qualified and become criterion for other, importantly noted as Knuth Morris Pratt and Boyer Moore which solves such problem in O(M + N) time. A quantum identical pattern matching algorithms are based upon the principle of high speed Grover's quantum search method which searches an element over unstructured database of "N" elements in O(root N) time. The standard quantum algorithm was proposed by Ramesh and Vinay that could obtain computational speed and provides solution in 0(root M + root N) time. We review two already existing quantum based exact and approximate pattern matching algorithms and then by combining the logic of both, a new exact pattern matching algorithm is being proposed which overcomes the algorithmic constraints and substantially proves to be equivalently better than the existing classical and quantum benchmark algorithms. So, this article includes existing and proposed quantum pattern matching algorithms, their flowcharts and examples, mathematical justification over complexity analysis. At last we discuss suitable application domains and further possible variations over the proposed work. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： pattern Matching Qubit Superposition Grover's Search Exact and Approximate Matching

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Diagnosis of Cerebellar Ataxia Based on Gait analysis Using Human Pose Estimation: A Deep Learning Approach

Diagnosis of Cerebellar Ataxia Based on Gait Analysis Using ...

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IEEE EMBS conference on Biomedical Engineering and Sciences (IECBES)

作者： Hisham Khalil Ahmed Mohamed Saad Emam Saad Uswah Khairuddin Center for Artificial Intelligence and Robotics Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computing Sciences Texas A&M University-Corpus Christi Corpus Christi TX USA

Human gait analysis has been one of the primary procedures for diagnosis in modern healthcare applications for various diseases. Instead of using expensive wearable sensors on patients, this research aims to assist in gait analysis and classification for medical diagnoses using computer vision solely. A long short-term memory (LSTM) neural network based on MediaPipe Pose for video-based human gait analysis is proposed to assist in diagnosing patients with neurodegenerative diseases, particularly cerebellar ataxia. The kinematic parameters were extracted from the pose estimation model on captured gait videos before deriving the spatiotemporal parameters for quantitative gait analysis. Data augmentation is applied to increase dataset size, and five-fold cross-validation is performed to verify the suitability of the developed dataset for training deep neural networks. The selected LSTM model achieves a testing accuracy of 99.8% with very high precision and recall metrics for ataxic and normal gait classes. The proposed methodology can be applied in broader applications for remote rehabilitation and patient monitoring. Clinical Relevance-The developed system can assist physicians in diagnosing cerebellar ataxic patients and monitoring gait rehabilitation process remotely via camera vision.

关键词： Training Deep learning Adaptation models Neural networks Pose estimation Cameras Data models

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Robot Swarm Navigation: Methods, analysis, and Applications

Robot Swarm Navigation: Methods, Analysis, and Applications

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Big Data & Artificial intelligence & Software Engineering (ICBASE), international conference on

作者： Zhaochen Sun School of Physics Xi’an Jiaotong University Xi’an Jiaotong University XJTU Xi’an China

ISBN: (纸本)9781665427104

Swarm navigation is one of the possible collective behaviours a swarm robotic system can possess. Research in multiple robot navigation has gained more attention given their potential real-world applications, such as search and rescue, transportation, precision farming, and environmental monitoring. In this paper, we analyze the recent advances in the field of swarm navigation, focusing mainly on design and analysis methods that contribute to collective exploration, coordinated motion, and collective transport. Moreover, various experimental and real-world applications of swarm navigation, instead of works that involve simulations, are described. Several challenges that restrict the implementation of successful laboratory works of swarm systems to industrial applications are also identified and described. To tackle these challenges, some interesting future research directions are proposed and discussed.

关键词： Navigation Service robots Robot kinematics Design methodology Transportation Swarm robotics Focusing

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