Confront Recognition in Mobile Devices

 Face Reputation in Mobile phones Essay

2010: 040 CIV

MASTER'S THESIS

Face Identification in Mobile Devices

Mattias Junered

Luleå University of Technology

MSc Programmes in Architectural

M edia Technology

M epartment of Computer Scientific research and Electrical Engineering

Division of Signal Finalizing

2010: 040 CIV -- ISSN: 1402-1617 - ISRN: LTU-EX--10/040--SE

Deal with Recognition in Mobile Devices

Mattias Junered

Luleå University of Technology

Mar 2, 2010

Abstract

The latest technological advancements have made encounter recognition a really viable identification and verification technique and one cause of its reputation is the non-intrusive nature of image buy. A photo can be had easily without the person actually being aware of the procedure. The interest in biometrics simply by several governments for discovering possible criminals or verifying users pertaining to access control is continuously increasing. Other industries can also be finding uses for face identification techniques just like in entertainment systems and then for robots that interact with individuals.

Mobile phones will be constantly enhancing and the majority are equipped with an electronic digital camera. This facilitates taking a large amount of images every day having a camera telephone instead of a stand-alone digital camera. Applying face identification techniques in these images makes it possible to conduct so called deal with tagging to tag images with the brands of the photographed persons. This is convenient for sorting photos, creating cds or locating images of only a specific person. Having a stand-alone mobile software on the phone that performs these types of face recognition tasks in recently captured images is usually an interesting principle. The system can be trained on the set of images containing encounters to become competent of automatically recognizing a person in the training collection. However , many users have got up to hundreds or even thousands of photos on their cell phones and teaching a system phoning around is prohibitively time-consuming about such gadgets today. Rather, face recognition can be performed within the client using already skilled data transferred from some type of computer (server). This approach shows guaranteeing results and intensely good success. This article covers several strategies that can improve results by causing the system more robust.

Acknowledgements

This kind of work will not have been possible without the support of Apostolos Georgakis as external director at Ericsson AB. Thank you to Jiong Sun with the EAB/TVV section for connecting the pieces in the phone application and additional help and support. The author would also like to thank the entire EAB/TV office for their cooperation in creating the internal photo database and all the interesting discussions. Finally, thanks to Josef Hallberg inner supervisor, Kåre Synnes reviewer, evaluator at Luleå University of Technology and everyone else whom helped out.

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Contents

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two

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Introduction

1 ) 1 Qualifications..

1 . 2 Chapter format

1 . three or more Objective...

1 . 4 Objective......

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Related work

2 . 1 Confront Recognition Vendor Test.

installment payments on your 2 Criteria categorization....

2 . 2 . 1 Projection methods...

2 . installment payments on your 2 Statistical methods...

installment payments on your 2 . several Graph corresponding methods

installment payments on your 2 . some Neural network methods

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6th

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Theory

several. 1 History..............

3. a couple of Pre-processing...

Bibliography: Notes in Computer Scientific research, vol. 3021. Springer, 2005, pp. 469–481.

[7] G. B. Huang, V. Jain, and E. Learned-Miller, " Unsupervised joint alignment of

complex images”, in Laptop Vision, 3 years ago

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