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Big Data Analytics on Customer Behaviors with Kinect Sensor Network
Hongye Zhong, Jitian Xiao
Pages - 36 - 47     |    Revised - 01-03-2015     |    Published - 31-03-2015
Volume - 6   Issue - 2    |    Publication Date - March 2015  Table of Contents
Big Data, Kinect, Customer, Behavior Analysis, Human Computer Interaction.
In modern enterprises, customer data is valuable for identifying their behavioral patterns and developing marketing strategies that can align with the preferences of different customers. The objective of this research is to develop a framework that promotes the use of Kinect sensors for Big Data Analytics on customer behavior analysis. Kinect enables 3D motion capture, facial recognition and voice recognition capabilities which allow to analyze customer behaviors in various aspects. Information fusion on the network of multiple Kinect sensors can achieve enhanced insight of the customer emotion, habits and consuming tendencies. Big Data Analytic techniques such as clustering and visualization are applied on the data collected from the sensors to provide better comprehension on the customers. Prediction on how to improve the customer relationship can be made to stimulate the vendition. Finally, an experimental system is designed based on the proposed framework as an illustration of the framework implementation.
CITED BY (1)  
1 Negahban, A., Kim, D. J., & Kim, C. (2016). Unleashing the Power of mCRM: Investigating Antecedents of Mobile CRM Values from Managers’ Viewpoint. International Journal of Human-Computer Interaction, (just-accepted).
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 TechRepublic 
5 Scribd 
6 SlideShare 
7 PdfSR 
A. Raorane & R.V. Kulkarni, "Data Mining Techniques: A Source for Consumer Behavior Analysis," Shahu Institute of business Education and Research, pp. 1-15, 2011.
A. Rotem-Gal-Oz, SOA Patterns, Manning Publications, 2012.
C. Ryu & H. Kim, "A Fast Fingerprint Matching Algorithm using Parzen Density," Lecture Notes in Computer Science, pp. 525-533, 2003.
C. WILLIAMS, "Prediction with Gaussian Processes: from Linear Regression to Linear Prediction and Beyond," Technical Report NCRG, pp. 1-13, 1997.
D.A. Hantula, V.K. Wells, Consumer Behavior Analysis, Routledge, 2013.
E. Blasch, E. Bosse & D.A. Lambert, High-Level Information Fusion Management and Systems Design, Artech House, 2012.
G. Valsh, Getting Started with NoSQL, Packt Publishing, 2013.
H. Zhang & G. Liu, "Research of Emotion Recognition Based on Pulse Signal," IEEE Computer, pp. 506-509, 2010.
J. Han, L. Shao, D. Xu & J. Shotton, "Enhanced Computer Vision with Microsoft Kinect Sensor: A Review," IEEE Computer, pp. 1318-1332, 2013.
M. Fitzpatrick & N. Matthiopoulos, "Real Time Person Tracking and Identification using the Kinect Sensor," Worcester Polytechnic Institute, 2013.
M. H. M. M. A.-M. A-Nasser Ansari, "Normalized 3D to 2D Model-based Facial Image Synthesis for 2D Model-based Face Recognization," IEEE Computer, pp. 178-181, 2011.
M. Han, "Customer Segmentation Model Based on Retail Consumer Behavior Analysis," IEEE Computer, pp. 914-917, 2008.
Mashable, "Watch Your Heartbeat on Xbox One’s New Kinect," [Online]. Available: http://mashable.com/2013/05/22/xbox-one-kinect-heartbeat/.
R. Min, N. Kose & J. Dugelay, "KinectFaceDB: A Kinect Database for Face Recognition," IEEE Computer, pp. 1534-1548, 2014.
S. Fukuda & V. Kostov, "Extracting Emotion from Voice," IEEE Computer, pp. 299-304, 1999.
S. Gangwar & K. Kumar, "3D Face Recognition Based On Extracting PCA Methods," International Journal of Engineering Research and Applications, pp. 693-696, 2012.
V. Prajapati, Big Data Analytics with R and Hadoop, Packt Publishing, 2013.
W. McKinney, Python for Data Analysis, O'Reilly Media Inc, 2013.
Mr. Hongye Zhong
School of Computer and Security Science Edith Cowan University WA, 6050 - Australia
Dr. Jitian Xiao
School of Computer and Security Science Edith Cowan University WA, 6050, Australia - Australia

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