RFID ACCESS AUTHORIZATION BY FACE
RECOGNITION
報告學生:翁偉傑
1 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 2
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。 3
SECURITY SYSTEM FOR RFID ACCESS CONTROL (1/2)
4
The Processes of Security System Training
SECURITY SYSTEM FOR RFID ACCESS CONTROL (2/2)
5
The Processes of the Security System
FACE FEATURE EXTRACTION WITH SIFT (1/3)
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範例:
SIFT 它的穩定性和精度高優於其他描述。
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)
8The Local Feature Descriptors of Two Images of Two Different People
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.
L-GEM TRAINED RBFNN BASED FACE RECOGNIZER (2/3)
10is a general framework to estimate the localized generalization error of a classifier
where N, Remp and SM denote the number of training samples, the training mean square error and the stochastic sensitivity measure of RBFNN, respectively.
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. Fetch the card owner’s RBFNN from the database
2. Face Detection by Adaboost and output a small image with face only
3. Extract local feature descriptor from the detected face image
4. Classify the face by the RBFNN trained by the L-GEM
5. If the face owner does not match the RFID card owner, alert security, otherwise end
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。
實驗 90次,分為 30 次 ( 個人 )和 60 次 ( 混合 ) 。12
EXPERIMENTAL RESULTS (2/4)
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Training Images of Person A
EXPERIMENTAL RESULTS (3/4)
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Moving in the entrance A face is detected
Tracing the detected face The Face leaving
EXPERIMENTAL RESULTS (4/4)
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Testing Results of the Security System
CONCLUSIONS
This research proposed a security system combining RFID card access control with face recognition by RBFNN trained by the L-GEM.
enhance security of RFID card based access control systems
Can only recognize one person per access.
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