中文版 | English
题名

姿势转换在生物特征识别中的应用研究

其他题名
RESEARCH ON THE APPLICATION OF POSTURAL TRANSITION IN BIOMETRICS
姓名
姓名拼音
YAO Junguang
学号
12132156
学位类型
硕士
学位专业
0809 电子科学与技术
学科门类/专业学位类别
08 工学
导师
王太宏
导师单位
电子与电气工程系
论文答辩日期
2024-05-10
论文提交日期
2024-06-15
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

基于步态的行为生物特征识别技术具有难以被模仿和不易被察觉的特点,因此被广泛应用于各种生物识别系统中。步态和姿势转换(Postural Transition, PT)都是完全由神经肌肉系统控制的行为。由于需要稳定人体重心,PT能够比步态更加深入地激活肌肉神经系统,因此PT可能可以更加好地反映个体行为差异。然而,基于PT的行为生物特征识别技术却很少被人研究。本研究评估了利用PT进行生物特征识别的潜在可行性,设计了一种基于PT的生物识别系统,该识别系统的特点是并行提取行为模板中具有鲁棒性和鉴别性的专家特征和深度特征,然后将两种特征融合以构建时不变的特征。为了验证所述系统设计了行为实验,通过在下背部固定惯性传感器的方法在一周内采集了22名用户的PT信息。实验表明,当严格划分数据集时,所述基于PT的识别系统的准确率达到了95.30%。将9个具有代表性的识别系统与所述系统进行了比较,识别准确率至少提高了15.93%。进一步地,大多数现有的行为生物特征认证方法只针对某种特定的行为,或者只针对单一记录时间的数据。针对上述问题本文还提出了一种同时适用于PT和步态的时不变生物特征认证系统。该系统的特点是基于孪生神经网络,利用距离度量计算成对样本融合特征的相似度量。结果表明,所述系统能够在累计663人的数据集上取得最低2.82%的等误差率(Equal Error Rate, EER)。值得注意的是,所述系统无需进行任何更改即可应用于从坐到站、从站到坐、平地行走、上坡、下坡五种具有差异的行为。这项研究展示了PT作为生物特征的应用潜力,并提供了应用参考。

关键词
语种
中文
培养类别
独立培养
入学年份
2021
学位授予年份
2024-06
参考文献列表

[1] YANG X, SHU L, LIU Y, et al. Physical Security and Safety of IoT Equipment: A Survey of Recent Advances and Opportunities [J]. IEEE Transactions on Industrial Informatics, 2022, 18(7): 4319-30.
[2] OHASHI K, OTA S, OHNO-MACHADO L, et al. Smart medical environment at the point of care: Auto-tracking clinical interventions at the bed side using RFID technology [J]. Computers in Biology and Medicine, 2010, 40(6): 545-54.
[3] JAIN A K, DEB D, ENGELSMA J J. Biometrics: Trust, but Verify [J]. Arxiv, 2021.
[4] LóPEZ J M E, CELDRáN A H, ESQUEMBRE F, et al. A Supervised ML Biometric Continuous Authentication System for Industry 4.0 [J]. IEEE Transactions on Industrial Informatics, 2022, 18(12): 9132-40.
[5] RAHMAN A, CHOWDHURY M E H, KHANDAKAR A, et al. Robust biometric system using session invariant multimodal EEG and keystroke dynamics by the ensemble of self-ONNs [J]. Computers in Biology and Medicine, 2022, 142: 13.
[6] ADVISORY M I R. 全球生物识别市场规模和份额分析-增长趋势和预测(2023-2028) [Z]. 2023
[7] JIANG X, XU K, LIU X, et al. Neuromuscular Password-Based User Authentication [J]. IEEE Transactions on Industrial Informatics, 2021, 17(4): 2641-52.
[8] FIGUEIREDO I N, MOURA S, NEVES J S, et al. Automated retina identification based on multiscale elastic registration [J]. Computers in Biology and Medicine, 2016, 79: 130-43.
[9] WANG D, HU Q, YANG C. Biometric recognition based on scalable end-to-end convolutional neural network using photoplethysmography: A comparative study [J]. Computers in Biology and Medicine, 2022, 147: 105654.
[10] ZHAO Z, ZHANG Y, DENG Y, et al. ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation [J]. Computers in Biology and Medicine, 2018, 102: 168-79.
[11] 秦皓. 基于步态序列的行人步态识别研究 [D], 2023.
[12] AILISTO H, LINDHOLM M, MANTYJARVI J, et al. Identifying people from gait pattern with accelerometers; proceedings of the Conference on Biometric Technology for Human Identification II, Orlando, FL, F Mar 28-29, 2005 [C]. Spie-Int Soc Optical Engineering: BELLINGHAM, 2005.
[13] JUHOLA M, ZHANG Y, RASKU J. Biometric verification of a subject through eye movements [J]. Computers in Biology and Medicine, 2013, 43(1): 42-50.
[14] MARTINEZ-DIAZ M, FIERREZ J, GALBALLY J, et al. Towards mobile authentication using dynamic signature verification: Useful features and performance evaluation; proceedings of the 2008 19th International Conference on Pattern Recognition, F 8-11 Dec. 2008, 2008 [C].
[15] SINGH J P, JAIN S, ARORA S, et al. A Survey of Behavioral Biometric Gait Recognition: Current Success and Future Perspectives [J]. Archives of Computational Methods in Engineering, 2021, 28(1): 107-48.
[16] WAN C S, WANG L, PHOHA V V. A Survey on Gait Recognition [J]. ACM Comput Surv, 2019, 51(5): 35.
[17] 黄浩华. 基于惯性传感器的身份认证与识别技术 [D], 2021.
[18] 李文娟. 基于深度神经网络的步态分析及在边缘智能终端的部署 [D], 2022.
[19] PEDOTTI A. A study of motor coordination and neuromuscular activities in human locomotion [J]. Biological Cybernetics, 1977, 26(1): 53-62.
[20] BIANCHI L, ANGELINI D, LACQUANITI F. Individual characteristics of human walking mechanics [J]. Pflugers Arch, 1998, 436(3): 343-56.
[21] TU B B, XU H, XIE X, et al. Gait Recognition Using Density-Based Outlier Detection and Location Fusion by Sparse Representation; proceedings of the International Conference on Energy, Power, Environment and Computer Application (ICEPECA), Wuhan, PEOPLES R CHINA, F Jan 20-21, 2019 [C]. Destech Publications, Inc: LANCASTER, 2019.
[22] ZHANG Y T, PAN G, JIA K, et al. Accelerometer-Based Gait Recognition by Sparse Representation of Signature Points With Clusters [J]. IEEE T Cybern, 2015, 45(9): 1864-75.
[23] NGO T T, MAKIHARA Y, NAGAHARA H, et al. The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication [J]. Pattern Recognit, 2014, 47(1): 228-37.
[24] KIM B, YOUM C, PARK H, et al. Association of Muscle Mass, Muscle Strength, and Muscle Function with Gait Ability Assessed Using Inertial Measurement Unit Sensors in Older Women [J]. International Journal of Environmental Research and Public Health, 2022, 19(16).
[25] SETHI D, BHARTI S, PRAKASH C. A comprehensive survey on gait analysis: History, parameters, approaches, pose estimation, and future work [J]. Artificial Intelligence in Medicine, 2022, 129.
[26] DE MARSICO M, MECCA A. A Survey on Gait Recognition via Wearable Sensors [J]. ACM Comput Surv, 2019, 52(4): 39.
[27] MONTERO-ODASSO M, VERGHESE J, BEAUCHET O, et al. Gait and Cognition: A Complementary Approach to Understanding Brain Function and the Risk of Falling [J]. Journal of the American Geriatrics Society, 2012, 60(11): 2127-36.
[28] LAHMIRI S. Gait Nonlinear Patterns Related to Parkinson’s Disease and Age [J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68(7): 2545-51.
[29] FARIA C D C D M, SALIBA V A, TEIXEIRA-SALMELA L F. Musculoskeletal biomechanics in sit-to-stand and stand-to-sit activities with stroke subjects: a systematic review [J]. Fisioterapia em Movimento, 2010, 23(1): 35-52.
[30] KUMAR V, YOSHIIKE T, SHIBATA T. Predicting Sit-to-Stand Adaptations due to Muscle Strength Deficits and Assistance Trajectories to Complement Them [J]. Frontiers in Bioengineering and Biotechnology, 2022, 10.
[31] ROBERT T, CAUSSE J, WANG X. Dynamics of sit-to-stand motions: effect of seat height, handle use and asymmetrical motions [J]. Computer Methods in Biomechanics and Biomedical Engineering, 2011, 14: 191-2.
[32] MILLOR N, LECUMBERRI P, GOMEZ M, et al. Gait Velocity and Chair Sit-Stand-Sit Performance Improves Current Frailty-Status Identification [J]. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(11): 2018-25.
[33] NISHIMURA T, ARIMA K, OKABE T, et al. Usefulness of chair stand time as a surrogate of gait speed in diagnosing sarcopenia [J]. Geriatrics & Gerontology International, 2017, 17(4): 659-61.
[34] BALTASAR-FERNANDEZ I, ALCAZAR J, LOSA-REYNA J, et al. Comparison of available equations to estimate sit-to-stand muscle power and their association with gait speed and frailty in older people: Practical applications for the 5-rep sit-to-stand test [J]. Experimental Gerontology, 2021, 156.
[35] MCCOOL J, DOBSON R, WHITTAKER R, et al. Mobile Health (mHealth) in Low- and Middle-Income Countries [J]. Annu Rev Public Health, 2022, 43: 525-39.
[36] NICKEL C, WIRTL T, BUSCH C. Authentication of Smartphone Users Based on the Way They Walk Using k-NN Algorithm; proceedings of the 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, F 18-20 July 2012, 2012 [C].
[37] GADALETA M, ROSSI M. IDNet: Smartphone-based gait recognition with convolutional neural networks [J]. Pattern Recognit, 2018, 74: 25-37.
[38] GAFUROV D, SNEKKENES E. Gait Recognition Using Wearable Motion Recording Sensors [J]. Eurasip Journal on Advances in Signal Processing, 2009.
[39] SUN F M, ZANG W L, GRAVINA R, et al. Gait-based identification for elderly users in wearable healthcare systems [J]. Inf Fusion, 2020, 53: 134-44.
[40] SUBRAMANIAN R, SARKAR S. Evaluation of Algorithms for Orientation Invariant Inertial Gait Matching [J]. IEEE Trans Inf Forensic Secur, 2019, 14(2): 304-18.
[41] LIN W T M, LIN B S, LEE I J, et al. Development of a Smartphone-Based mHealth Platform for Telerehabilitation [J]. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 2682-91.
[42] SUNSHINE J. Smart Speakers: The Next Frontier in mHealth [J]. JMIR Mhealth Uhealth, 2022, 10(2): e28686.
[43] XING Y X, WANG T, ZHOU F, et al. EVAL Cane: Nonintrusive Monitoring Platform With a Novel Gait-Based User-Identification Scheme [J]. Ieee Transactions on Instrumentation and Measurement, 2021, 70.
[44] ZHANG M Y, LIU D, WANG Q S, et al. Gait Pattern Recognition Based on Plantar Pressure Signals and Acceleration Signals [J]. Ieee Transactions on Instrumentation and Measurement, 2022, 71.
[45] CONTI M, ZACHIA-ZLATEA I, CRISPO B. Mind howyou answer me!:Transparently authenticating the user of a smartphone when answering or placing a call [M]. 2011.
[46] YANG L, GUO Y, DING X, et al. Unlocking Smart Phone through Handwaving Biometrics [J]. Ieee Transactions on Mobile Computing, 2015, 14(5): 1044-55.
[47] SITOVA Z, SEDENKA J, YANG Q, et al. HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users [J]. IEEE Trans Inf Forensic Secur, 2016, 11(5): 877-92.
[48] BURIRO A, CRISPO B, ZHAUNIAROVICH Y, et al. Please Hold On: Unobtrusive User Authentication using Smartphone's built-in Sensors; proceedings of the IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), New Delhi, INDIA, F Feb 22-24, 2017 [C]. 2017.
[49] KILIC S, ASKERZADE I, KAYA Y. Using ResNet Transfer Deep Learning Methods in Person Identification According to Physical Actions [J]. Ieee Access, 2020, 8: 220364-73.
[50] AMROUN H, AMMI M. Who Used My Smart Object? A FlexibleA pproach for the Recognition of Users [J]. Ieee Access, 2018, 6: 7112-22.
[51] LIU S J, CHEN Y R, WANG H, et al. A Low-Calculation Contactless Continuous Authentication Based on Postural Transition [J]. IEEE Trans Inf Forensic Secur, 2022, 17: 3077-90.
[52] BOLINK S, NAISAS H, SENDEN R, et al. Validity of an inertial measurement unit to assess pelvic orientation angles during gait, sit-stand transfers and step-up transfers: Comparison with an optoelectronic motion capture system [J]. Medical Engineering & Physics, 2016, 38(3): 225-31.
[53] CARRION-OJEDA D, FONSECA-DELGADO R, PINEDA I. Analysis of factors that influence the performance of biometric systems based on EEG signals [J]. Expert Systems with Applications, 2021, 165.
[54] DARGAN S, KUMAR M. A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities [J]. Expert Systems with Applications, 2020, 143.
[55] PERMATASARI J, CONNIE T, ONG T S, et al. Adaptive 1-dimensional time invariant learning for inertial sensor-based gait authentication [J]. Neural Computing and Applications, 2023, 35(3): 2737-53.
[56] SUBRAMANIAN R, SARKAR S, LABRADOR M, et al. Orientation invariant gait matching algorithm based on the Kabsch alignment; proceedings of the IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015), F 23-25 March 2015, 2015 [C].
[57] ALAWNEH L, AL-ZINATI M, AL-AYYOUB M. User identification using deep learning and human activity mobile sensor data [J]. Int J Inf Secur, 2023, 22(1): 289-301.
[58] MEKRUKSAVANICH S, JITPATTANAKUL A. Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing [J]. Sensors, 2021, 21(22): 21.
[59] MEKRUKSAVANICH S, JITPATTANAKUL A. Deep Residual Network for Smartwatch-Based User Identification through Complex Hand Movements [J]. Sensors, 2022, 22(8): 24.
[60] INCEL O D, GUNAY S, AKAN Y, et al. DAKOTA: Sensor and Touch Screen-Based Continuous Authentication on a Mobile Banking Application [J]. Ieee Access, 2021, 9: 38943-60.
[61] VAN HAMME T, PREUVENEERS D, JOOSEN W. Improving Resilience of Behaviometric Based Continuous Authentication with Multiple Accelerometers; proceedings of the 31st Annual IFIP WG 113 Conference on Data and Applications Security and Privacy (DBSec), Philadelphia, PA, F Jul 19-21, 2017 [C]. Springer International Publishing Ag: CHAM, 2017.
[62] BANOS O, TOTH M A, DAMAS M, et al. Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition [J]. Sensors, 2014, 14(6): 9995-10023.
[63] ANGUITA D, GHIO A, ONETO L, et al. A Public Domain Dataset for Human Activity Recognition using Smartphones; proceedings of the The European Symposium on Artificial Neural Networks, F, 2013 [C].
[64] SHOAIB M, SCHOLTEN H, HAVINGA P J M, et al. A Hierarchical Lazy Smoking Detection Algorithm Using Smartwatch Sensors; proceedings of the 18th IEEE International Conference on e-Health Networking, Applications and Services (Healthcom), Munich, GERMANY, F Sep 14-16, 2016 [C]. Ieee: NEW YORK, 2016.
[65] ZHANG C, LIU W, MA H D, et al. SIAMESE NEURAL NETWORK BASED GAIT RECOGNITION FOR HUMAN IDENTIFICATION; proceedings of the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, PEOPLES R CHINA, F Mar 20-25, 2016 [C]. Ieee: NEW YORK, 2016.
[66] HOWARTH S J, HUM R, EAD L. A Kinematic Comparison Between Sit-to-Stand Movements and Individual Cycles of the 5-Cycle Sit-to-Stand Test [J]. J Manip Physiol Ther, 2021, 44(6): 487-96.
[67] JOB M, BATTISTA S, STANZANI R, et al. Quantitative Comparison of Human and Software Reliability in the Categorization of Sit-to-Stand Motion Pattern [J]. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 770-6.
[68] STYLIOS I, KOKOLAKIS S, THANOU O, et al. Behavioral biometrics & continuous user authentication on mobile devices: A survey [J]. Inf Fusion, 2021, 66: 76-99.
[69] RODRIGUEZ-MARTIN D, SAMA A, PEREZ-LOPEZ C, et al. SVM-based posture identification with a single waist-located triaxial accelerometer [J]. Expert Systems with Applications, 2013, 40(18): 7203-11.
[70] MILLOR N, LECUMBERRI P, GOMEZ M, et al. Kinematic Parameters to Evaluate Functional Performance of Sit-to-Stand and Stand-to-Sit Transitions Using Motion Sensor Devices: A Systematic Review [J]. IEEE Trans Neural Syst Rehabil Eng, 2014, 22(5): 926-36.
[71] AISSAOUI R, GANEA R, AMINIAN K. Conjugate momentum estimate using non-linear dynamic model of the sit-to-stand correlates well with accelerometric surface data [J]. J Biomech, 2011, 44(6): 1073-7.
[72] KERR K M, WHITE J A, BARR D A, et al. Analysis of the sit-stand-sit movement cycle in normal subjects [J]. Clinical Biomechanics, 1997, 12(4): 236-45.
[73] MILLOR N, LECUMBERRI P, GOMEZ M, et al. Automatic Evaluation of the 30-s Chair Stand Test Using Inertial/Magnetic-Based Technology in an Older Prefrail Population [J]. Ieee Journal of Biomedical and Health Informatics, 2013, 17(4): 820-7.
[74] MILLOR N, LECUMBERRI P, GOMEZ M, et al. An evaluation of the 30-s chair stand test in older adults: frailty detection based on kinematic parameters from a single inertial unit [J]. Journal of Neuroengineering and Rehabilitation, 2013, 10.
[75] DOHENY E P, FAN C W, FORAN T, et al. An instrumented sit-to-stand test used to examine differences between older fallers and non-fallers; proceedings of the 33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS), Boston, MA, F Aug 30-Sep 03, 2011 [C]. Ieee: NEW YORK, 2011.
[76] AYDEMIR E, TUNCER T, DOGAN S, et al. A novel biometric recognition method based on multi kernelled bijection octal pattern using gait sound [J]. Applied Acoustics, 2021, 173: 107701.
[77] JANSSEN W G M, KULCU D G, HOREMANS H L D, et al. Sensitivity of Accelerometry to Assess Balance Control During Sit-to-Stand Movement [J]. IEEE Trans Neural Syst Rehabil Eng, 2008, 16(5): 479-84.
[78] LAWHERN V J, SOLON A J, WAYTOWICH N R, et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces [J]. Journal of Neural Engineering, 2018, 15(5).
[79] LIAW R, LIANG E, NISHIHARA R, et al. Tune: A Research Platform for Distributed Model Selection and Training [J]. ArXiv, 2018, abs/1807.05118.
[80] ROLDAN-JIMENEZ C, CUESTA-VARGAS A I, BENNETT P. Assessing trunk flexo-extension during sit-to-stand test variant in male and female healthy subjects through inertial sensors [J]. Physician and Sportsmedicine, 2019, 47(2): 152-7.
[81] RYCKEWAERT G, DELVAL A, BLEUSE S, et al. Biomechanical mechanisms and centre of pressure trajectory during planned gait termination [J]. Neurophysiologie Clinique-Clinical Neurophysiology, 2014, 44(2): 227-33.
[82] YAN K, ZHANG L, WU H-C. Advanced Homological Analysis for Biometric Identification Using Accelerometer [J]. Ieee Sensors Journal, 2021, 21(6): 7954-63.
[83] WOŹNIAK M, WIECZOREK M, SIŁKA J. BiLSTM deep neural network model for imbalanced medical data of IoT systems [J]. Future Generation Computer Systems, 2023, 141: 489-99.
[84] CHAKI J, WOŹNIAK M. Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review [J]. Biomedical Signal Processing and Control, 2023, 80: 104223.
[85] SAINATH T N, VINYALS O, SENIOR A, et al. Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks; proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), F 19-24 April 2015, 2015 [C].
[86] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-324.
[87] TUNCER T, DOGAN S. Novel dynamic center based binary and ternary pattern network using M4 pooling for real world voice recognition [J]. Applied Acoustics, 2019, 156: 176-85.
[88] WANG C, XIAO Y R, GAO X, et al. A Framework for Behavioral Biometric Authentication Using Deep Metric Learning on Mobile Devices [J]. Ieee Transactions on Mobile Computing, 2023, 22(1): 19-36.
[89] BRAJDIC A, HARLE R, ASSOC COMP M. Walk Detection and Step Counting on Unconstrained Smartphones; proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), ETH Zurich, Zurich, SWITZERLAND, F Sep 08-12, 2013 [C]. Assoc Computing Machinery: NEW YORK, 2013.
[90] NGO T T, MAKIHARA Y, NAGAHARA H, et al. Similar gait action recognition using an inertial sensor [J]. Pattern Recognit, 2015, 48(4): 1289-301.
[91] BROMLEY J, GUYON I, LECUN Y, et al. Signature verification using a "Siamese" time delay neural network [Z]. Proceedings of the 6th International Conference on Neural Information Processing Systems. Denver, Colorado; Morgan Kaufmann Publishers Inc. 1993: 737–44
[92] MO J H, KUMAR R, IEEE. iCTGAN-An Attack Mitigation Technique for Random-vector Attack on Accelerometer-based Gait Authentication Systems; proceedings of the IEEE International Joint Conference on Biometrics (IJCB), Abu Dhabi, U ARAB EMIRATES, F Oct 10-13, 2022 [C]. Ieee: NEW YORK, 2022.

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姚俊光. 姿势转换在生物特征识别中的应用研究[D]. 深圳. 南方科技大学,2024.
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