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RFID ACCESS
AUTHORIZATION BY FACE
RECOGNITION
報告學生：翁偉傑
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Proceedings of the Eighth International
Conference on Machine Learning and Cybernetics,
Baoding, 12-15 July 2009
OUTLINE

Introduction

Security System for RFID Access Control

Face Feature Extraction with SIFT

L-GEM Trained RBFNN based Face Recognizer

Experimental Results

Conclusions
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INTRODUCTION



Radio Frequency Identification (RFID)的主要缺點，
任何人都可以得到該卡存取。
本研究提出了一種基於神經網絡的人臉識別系統，
本研究提出了一個Localized Generalization Error
Model (L-GEM)的徑向Radial Basis Function
Neural Network (RBFNN) 人臉辨識系統，以提高
安全性的RFID卡的系統。
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SECURITY SYSTEM FOR RFID ACCESS
CONTROL (1/2)
The Processes of Security System Training
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SECURITY SYSTEM FOR RFID ACCESS
CONTROL (2/2)
The Processes of the Security
System
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FACE FEATURE EXTRACTION WITH SIFT
(1/3)
SIFT 它的穩定性和精度高優於其他描述。
範例：
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FACE FEATURE EXTRACTION WITH SIFT
(2/3)
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Local Feature Descriptors of Two Images of the Same Person
FACE FEATURE EXTRACTION WITH SIFT
(3/3)
The Local Feature Descriptors of Two Images of Two Different People
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L-GEM TRAINED RBFNN BASED FACE
RECOGNIZER (1/3)
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The RBFNN is trained as follows:
1.
Store the RFID card owner information in the database
and take 10 images of the owner
2.
L-GEM trained RBFNN to recognize face of person.
3.
The trained RBFNN is then stored in the database and
associated with the card owner.
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L-GEM TRAINED RBFNN BASED FACE
RECOGNIZER (2/3)
where N, Remp and SM denote the number of training
samples, the training mean square error and the stochastic
sensitivity measure of RBFNN, respectively.
is a general framework to estimate the localized
generalization error of a classifier
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L-GEM TRAINED RBFNN BASED FACE
RECOGNIZER (3/3)
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The procedures for the face recognition after reading
the RFID card ID is as follows:
1.
2.
3.
4.
5.
Fetch the card owner’s RBFNN from the database
Face Detection by Adaboost and output a small image
with face only
Extract local feature descriptor from the detected face
image
Classify the face by the RBFNN trained by the L-GEM
If the face owner does not match the RFID card owner,
alert security, otherwise end
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EXPERIMENTAL RESULTS (1/4)

In this experiment, we assume that there are 3
users of the security system. Each of them holds an
RFID card. The system is built to verify whether the
card holder is the card owner. We name these 3
people as Person A, Person B and Person C for
convenience.

以10張為訓練圖像。
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實驗90次，分為30次(個人)和60次(混合)。
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EXPERIMENTAL RESULTS (2/4)
Training Images of Person A
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EXPERIMENTAL RESULTS (3/4)
Moving in the entrance
A face is detected
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Tracing the detected face
The Face leaving
EXPERIMENTAL RESULTS (4/4)
Testing Results of the Security System
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CONCLUSIONS

This research proposed a security system
combining RFID card access control with face
recognition by RBFNN trained by the L-GEM.
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enhance security of RFID card based access
control systems

Can only recognize one person per access.
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