Cloud Based Face Recognition for Google Glass

Face recognition applications can benefit from the cloud computing as they become widely available and easy to acquire today. There are numerous applications of face recognition in terms of security, assistance, guidance and so on. By performing the face recognition on cloud, we can greatly reduce the processing time and clients will not have to store the big data for the image verification on their local machine (cell phones, pc's etc). Cloud computing increases the processing power and storage with very less cost comparing to the cost of acquiring an equally strong server machine. In this research the plan is to enhance the user experience of augmented display wearing google glass, and for doing that, this system is being proposed in which a person wearing google glass will send an image of a person to cloud server powered by Hadoop (open-source software for reliable, scalable, distributed computing) cloud server will recognize the face from the database already present on server and then response to client device (google glass). Then google glass will display the face details in a form of augmented display to the person wearing them. By moving the face recognition process on cloud, the device will require less processing power, and by having the database on cloud server, multiple clients will no longer require to maintain their local database.

[1]  Siti Hafizah Ab Hamid,et al.  Mobile storage augmentation in mobile cloud computing: Taxonomy, approaches, and open issues , 2015, Simul. Model. Pract. Theory.

[2]  Shrinivas B. Joshi,et al.  Apache hadoop performance-tuning methodologies and best practices , 2012, ICPE '12.

[3]  Ioannis A. Kakadiaris,et al.  Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Saqib Ali,et al.  Cloud Computing: Virtual Web Hosting on Infrastructure as a Service (IaaS) , 2017, MSN.

[5]  Andrea F. Abate,et al.  2D and 3D face recognition: A survey , 2007, Pattern Recognit. Lett..

[6]  Rohitash Chandra,et al.  Face detection and recognition in an unconstrained environment for mobile visual assistive system , 2017, Appl. Soft Comput..

[7]  Baochang Zhang,et al.  Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor , 2010, IEEE Transactions on Image Processing.

[8]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  S. S. Islam,et al.  Next generation of computing through cloud computing technology , 2012, 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[10]  Hongxu Ma,et al.  Deploying and researching Hadoop in virtual machines , 2012, 2012 IEEE International Conference on Automation and Logistics.

[11]  Prashant Pandey,et al.  Cloud Analytics: Do We Really Need to Reinvent the Storage Stack? , 2009, HotCloud.

[12]  Omprakash Gnawali,et al.  Person-of-interest detection system using cloud-supported computerized-eyewear , 2013, 2013 IEEE International Conference on Technologies for Homeland Security (HST).

[13]  Daniel Lélis Baggio,et al.  Mastering OpenCV with Practical Computer Vision Projects , 2012 .

[14]  Hong Z. Tan,et al.  Exploring How Haptics Contributes to Immersion in Virtual Reality , 2016 .

[15]  Alva Erwin,et al.  Processing performance on Apache Pig, Apache Hive and MySQL cluster , 2014, Proceedings of International Conference on Information, Communication Technology and System (ICTS) 2014.

[16]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[17]  Mahadev Satyanarayanan,et al.  Towards wearable cognitive assistance , 2014, MobiSys.

[18]  Weidong Shi,et al.  Forensics-as-a-Service (FaaS): Computer Forensic Workflow Management and Processing Using Cloud , 2013, CLOUD 2013.

[19]  Shah Atiqur Rahman,et al.  Unintrusive eating recognition using Google Glass , 2015, 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth).

[20]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[21]  Zhihan Lv,et al.  Hand-free motion interaction on Google Glass , 2014, SIGGRAPH ASIA Mobile Graphics and Interactive Applications.

[22]  Jeff Tang The Mirror API , 2014 .

[23]  Ladislav Lenc,et al.  Automatic face recognition system based on the SIFT features , 2015, Comput. Electr. Eng..

[24]  Jae-Ho Chung,et al.  Face recognition using Fisherface algorithm and elastic graph matching , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).