Computation of Person Re-identification using Self-Learning Anomaly Detection Framework in Deep-Learning
J.Gowthamy1, Seeram Kiran Swamy2, M.P.Shaam Kumar3, M.Dhanush Kumar4
1J.Gowthamy, SRM Institute of Science and Technology, Ramapuram Chennai.
2A.Vidhyavani, SRM Institute of Science and Technology, Ramapuram Chennai.
3M.P.Shaam Kumar, SRM Institute of Science and Technology, Ramapuram Chennai.
4M.Dhanush Kumar, SRM Institute of Science and Technology, Ramapuram Chennai.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 1106-1109 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4385119119/2019©BEIESP | DOI: 10.35940/ijitee.A4385.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper proposes an application of a self-Learning anomaly detection framework in Deep-learning. In this application, both hybrid unsupervised and supervised machine learning schemes are used. Firstly, it takes metadata of the unsupervised data clustering module (DCM). Data clustering module (DCM) analyses the pattern of the monitoring data and enables the self-learning capability that eliminates the requirement of the prior knowledge of the abnormal network behaviors and also has the potential to detect the unforeseen anomalies. Next, we use the self-learning mechanism that transfer pattern learned by the DCM to a supervised data regression and classification module (DRCM) it’s Complexity is mainly related to scalability of supervised learning module. It is more measurable and less time consuming for online anomalies by avoiding excessively usage of the original dataset. It has a density-based clustering algorithm and deep learning, neural network structure-based DCM and DRCM. We are also using an anti-spoofing-based approach for presentation attack detection (PAD). In these approaches, we are mainly detecting a person reidentify and computing without having any false anomalies.
Keywords: Anomaly Detection Framework, Clustering module, Data Clustering Module (DCM), Data Regression Clustering Module (DRCM), Multi-channel Sensors, Anti-spoofing
Scope of the Article: Clustering