Detection of a Hand Holding a Cellular Phone Using Multiple Image Features

Authors:
Hiroto Nagayoshi;Takashi Watanabe;Tatsuhiko Kagehiro;Hisao Ogata;Tsukasa Yasue;Hiroshi Sako
Affiliations:
Central Research Laboratory, Hitachi Ltd., Tokyo, Japan;Central Research Laboratory, Hitachi Ltd., Tokyo, Japan;Central Research Laboratory, Hitachi Ltd., Tokyo, Japan;Hitachi-Omron Terminal Solutions, Corp., Aichi, Japan;Hitachi-Omron Terminal Solutions, Corp., Aichi, Japan;Central Research Laboratory, Hitachi Ltd., Tokyo, Japan
Venue:
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Year:
2009

Citing 7
Cited 0

Hand Detection and Tracking Using Pixel Value Distribution Model for Multiple-Camera-Based Gesture Interactions

KMN '02 Proceedings of the IEEE Workshop on Knowledge Media Networking
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
WaldBoost " Learning for Time Constrained Sequential Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Open Hand Detection in a Cluttered Single Image using Finger Primitives

CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
An iterative image registration technique with an application to stereo vision

IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Robust hand detection

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A boosted classifier tree for hand shape detection

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Detection of a hand holding a cellular phone was developed to recognize whether someone is using a cellular phone while operating an automated teller machine (ATM). The purpose is to prevent money transfer fraud. Since a victim is told a bogus reason to transfer money and how to operate the machine through a cellular phone, detecting a working cellular phone is necessary. However, cellular phone detection was not realistic due to variable colors and shapes. We assumed that a user's hand beside the face was holding a cellular phone and decided to detect it. The proposed method utilizes color, shape, and motion. Color and motion were used to compare the input to the face. Shape was used to compare the input to the standard hand pattern. The experimental result was a detection rate of 90.0% and a false detection rate of 3.2%, where 7,324 and 20,708 images were used respectively.