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Research Articles

A biometric-based system for unsupervised anomaly behaviour detection at the pawn shop

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Pages 338-356 | Received 20 Sep 2021, Accepted 18 Jul 2022, Published online: 29 Jul 2022

References

  • Teichmann E FMJ, Falker MC. Money laundering – the gold method. J Money Laund Control. 2020;ahead-of-print(ahead–of–print). DOI: 10.1108/JMLC-07-2019-0060.
  • Cao Z, Hidalgo G, and Simon E T, et al. OpenPose: realtime multi-person 2D pose estimation using part affinity fields Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
  • Dentamaro V, Convertini VN, and Galantucci S, et al. Ensemble consensus: an unsupervised algorithm for anomaly detection in network security data, ITASEC, Italy. 2021 Italian Conference on Cybersecurity, 7-9 April, p. 309–318.
  • Viola E P, and Jones M. Rapid object detection using a boosted cascade of simple features. Comput Vision Pattern Recognit. 2001;1:I–I .
  • Liu W, Anguelov D, Erhan D, et al. Ssd: single shot multibox detector, European Conference on Computer Vision (ECCV); 2016, p. 21–37.
  • Mita T, Kaneko E T, Hori O. Joint haar-like features for face detection, Proceedings of the Tenth IEEE International Conference on Computer Vision; 2005.
  • Luttrell JB, Zhou Z, Zhang C, et al. Facial recognition via transfer learning: fine-tuning keras-vggface, Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI; 2017, p. 576–579.
  • Jiao Z, Qiao F, Yao N, et al. An ensemble of VGG networks for video-based facial expression recognition. 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia; 2018, 8470338, 2018.
  • Muro-De-La-Herran A, Garcia-Zapirain E B, Mendez-Zorilla A. Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors. 2014;14(2):3362–3394.
  • Wu Z, Huang Y, Wang L, et al. A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Trans Pattern Anal Mach Intell. 2017;39(2):209–226.
  • Zhang C, Liu W, Ma E H, et al., «Siamese neural network based gait recognition for human identification,» ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, p. 2832–2836, 2016.
  • Badave E H, Kuber M. Evaluation of person recognition accuracy based on openpose parameters. Proceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS; 2021;2021, p. 635–640.
  • Datcheva A, Elia E P, and Ross A. What else does your biometric data reveal: a survey on soft biometrics. IEEE Trans Inf Forensics Secur. 2016. 11(3):441–467.
  • Syed Idrus SZ, Cherrier, C E, Rosenberger C P. Bours. soft biometrics for keystroke dynamics: profiling individuals while typing passwords. Comput Secur. 2014;45:147–155.
  • Patruno C, Marani R, Cicirelli G, E. Stella, T. D’Orazio. People re-identification using skeleton standard posture and color descriptors from RGB-D data. Pattern Recogn. 2019;89:77–90.
  • Gogoi P, Bhattacharyya DK, Borah E B, et al. A survey of outlier detection methods in network anomaly identification. Comp J. 2011;54(4):570–588.
  • Koufakou E A, Georgiopoulos M. A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes. Data Min Knowl Discov. 2010;20(20):259–289.
  • Salzberg SL. «C4.5: programs for machine learning,». Mach. Learn. 1994;16(3):235–240.
  • Suykens E JA, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett. 1999;9(3):293–300.
  • Ghoting A, Otey E ME, Parthasarathy S. Loaded: link-based outlier and anomaly detection in evolving data sets. Proceedings of the 4th IEEE International Conference on Data, p. 387–390, 2004.
  • Du Y, Zhang E R, and Guo Y. A useful anomaly intrusion detection method using variable-length patterns and average hamming distance. J Comput. 2010;5(8):1219–1226.
  • Breunig MM, Kriegel HP, Ng E RT, et al. Lof: identifying density-based local outliers. ACM SIGMOD. 2000;29(2):93–104.
  • Benali L, Notton G, Fouilloy A, et al. Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components. Renewable Energy. 2019;132:871–884.
  • Zhang K, Zhang Z, Li E Z, et al. Joint face detection and alignment using multi-task cascaded convolutional networks. ArXiv. 2016.
  • Parkhi OM, Vedaldi E A, Zisserman A. Deep face recognition. Brit Mach Vision Conf. 2015;13: 1–12.
  • Krizhevsky A, Sutskever E I, and Hinton GE. ImagineNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012:25:1097–1105.
  • Wan L, Liu N, Huo E H, et al. Face Recognition with Convolutional Neural Networks and subspace learning. 2nd International Conference on Image, Vision and Computing (ICIVC), p. 228–233, 2017.
  • Cao Z, Simon T, Wei E SE, et al. Realtime multi-person 2d pose estimation using part affinity fields. Proceedings of the IEEE conference on computer vision and pattern recognition, p. 7291–7299, 2017.
  • Ester M, Kriegel HP, and Sander E J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise. KDD-96. 1996;96(34).
  • ChokePoint Dataset. sourceforge. [Online]. [updated 27 April 2022]. Available from: http://arma.sourceforge.net/chokepoint/. [ Consultato il giorno].
  • Wong Y, Chen S, Mau S, et al. Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. IEEE Biometrics Workshop, Computer Vision and Pattern Recognition (CVPR) Workshops, p. 81–88, 2011.
  • Choi J, Gao C, Messou E JCE, et al. Why can’t i dance in the mall? Learning to mitigate scene bias in action recognition. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada., 2019.
  • Karkkainen E K, and Joo J. FairFace: face attribute dataset for balanced race, gender, and age. Arxiv Pre Prints. 2021.
  • Koo JH, Cho SW, Beak NR, et al. CNN-based multimodal human recognition in surveillance environments. Sensors. 2018;18(9):3040.
  • Koo JH, Cho SW, Beak E NR, et al. Face and body-based human recognition by GAN-based blur restoration. Sensors. 2020;20(18):5229.
  • N. R AP, Alling ST. Face recognition: a tutorial on computational aspects. Emerging research surrounding power consumption and performance issues in utility computing, p. 405–425, 2016.