• Save
Ubiquitious Computing system : Integrating RFID with Face Recognition systems
Upcoming SlideShare
Loading in...5

Ubiquitious Computing system : Integrating RFID with Face Recognition systems






Total Views
Views on SlideShare
Embed Views



1 Embed 1

http://www.linkedin.com 1



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

Ubiquitious Computing system : Integrating RFID with Face Recognition systems Ubiquitious Computing system : Integrating RFID with Face Recognition systems Presentation Transcript

  • Ubiquitious Computing system : IntegratingUbiquitious Computing system : Integrating RFID with Face Recognition systemsRFID with Face Recognition systems Shahryar Ali Bahria University – Final year Project BS- Telecom
  • Project OverviewProject Overview  Student Attendance System using RFID and Face Recognition  RFID Tag (Card) for each Student  Student passes his tag through the RFID Reader for Attendance  Camera is used to capture the image of the student.  Face Recognition (PCA) is used for student verification.
  • Benefits and FeaturesBenefits and Features Automatic Attendance Records Entry Time of Students No Class Attendance Sheets No Need to Submit Attendance Sheets Efficient and Error free
  • Introduction to Radio FrequencyIntroduction to Radio Frequency Identification (RFID)Identification (RFID)  Automatic identification technology  Radio frequency (RF) waves  Reads Encoded Digital Data  Barcode v.s RFID  Applications in Military, Government agencies, Library and Time and access control.
  • Components of RFID System:  RFID Tag  RFID Reader/Interrogator  RFID Middleware/Host PC Types of RFID System: 1. Active vs. Passive 2. Read Only vs. Read/Write
  • Working of RFID SystemWorking of RFID System  Methods of Power Transfer  Magnetic Induction  Electromagnetic wave capture  Near field v.s Far field Communication  Passive RFID Basics  Database Management  Application
  • Frequency RangesFrequency Ranges FREQUENCY RANGE BAND READ RANGE ADVANTAGES DISADVANATAGE S APPLICATIONS LF 125-135 kHz Below .5 meter Accepted Worldwide Short read range, slow read rates Access Control , Animal Tracking HF 13.56 MHz Below 1 meter Quick read rates Require high power Item tracking, libraries UHF 860-969 MHz 3 meters High read range, very quick read rates Doesn’t operate well near water or metals Supply chain, parking lot access Microwave 2.45 GHz 1 meter Fastest read range Clear path required Supply chain
  • RFID StandardsRFID Standards ISO STANDARD CATEGORY DEFINED TOPICS ISO 11784 Animal Tracking Frequency, Baud-Rate, Code Structure ISO 18000 Air Interface Standard Frequency, Anti-collision Protocol ISO/IEC 14443 Proximity Cards Frequency, RF Power, Physical layer ISO 15693 Vicinity Cards Frequency, RF Power, Physical layer ISO 15691 Supply Chain and Item Management Data Processing , Application Commands ISO 18047-4 Testing Purposes System Functionality
  • Selection of RFID system:  RFID Reader Selection Criteria  RFID Tag Selection Criteria RFID System for the Implementation:  PUA-310-0 Proximity Reader  Standard Light Proximity Card
  • Reader SpecificationsReader Specifications Brand Pegasus Model PUA-310-0 Country of Origin Taiwan Reading Range 5cm Suitable Temperature -10 to 55 °C Power supply 12 V DC Operating Frequency 125 KHz Modulation Type ASK Interface RS-232 Dimension 116 L x 76W x 13H mm Weight 250grams Cost Rs.7000
  • Serial Communication(RS-232)Serial Communication(RS-232)  RS-232  DTE and DCE  Connectors  Data format  Baud-Rate DTE Pin2 Pin3 Pin2 DCE Pin3 RD TD TD RD
  • Installation and TestingInstallation and Testing Installation: • Environmental Analysis • Cabling • Transmission Rate • Power Testing : • HyperTerminal Settings • Reader Output
  •  12-digit ASCII code  “Start of Text” O2H and “End of Text” O3H  HEX Equivalent: 02H , 30H, 36H , 30H, 30H, 33H, 33 H, 32H, 35H, 31H, 41H, 03H  Total Card Length is 12 Bytes(96-bit)
  • Implementation of StudentImplementation of Student Attendance SystemAttendance System  Application Overview  Tool for Implementation  Serial Communication in MATLAB  Database of RFID Tags in MATLAB  Programming the Application  Demonstration in MATLAB GUI  Recording Information in Excel Sheet
  • Programming the ApplicationProgramming the Application  Key-less System  Start of Attendance  Start Time of Class  Student Attendance  Security  Student Entry Time  End of Attendance  End Time of Class
  • Demonstration in MATLAB GUIDemonstration in MATLAB GUI
  • Recording Information in Excel SheetRecording Information in Excel Sheet
  • Problem and the Need of FaceProblem and the Need of Face RecognitionRecognition  Illegal use of RFID Card  Not considered in Time and Access Control systems.  Use of Biometrics  Real-time Face Recognition System
  • Introduction to Face RecognitionIntroduction to Face Recognition  Unique facial features  Identification  Security Systems  Real-time `  Image Processing
  • How Face Recognition SystemHow Face Recognition System Works?Works? Image Acquisition Face Detection Feature Extraction Feature Matching Face Database Face Classification
  • Methods of Face Recognition:  Principal Component Analysis (PCA)  Independent Component Analysis (ICA)  Linear Discriminant Analysis (LDA) Problems in Face Recognition:  Variation in Scale  Variation in Orientation  Variation in illumination  Variation in expression
  • Face Recognition using PCAFace Recognition using PCA  Background Work  Method of PCA
  • Recognition of FacesRecognition of Faces  Representation of Images  Face Databases • The Color FERET Database • The Yale Face Database • The ORL Database of Faces
  • Flow Diagram (PCA)Flow Diagram (PCA) Image Acquisition (Face Database) Normalization of facial Images Calculation of Mean Image Calculation of EigenFaces Calculation of Weights of EigenFaces Unknown Input Image Normalization Calculate the Euclidean Distance Calculation of weight of input Face Image Classification of Input Face Image
  • Eigenfaces MethodEigenfaces Method  Training Set  Test Image  Eigenfaces  Mathematical procedure
  • Transformation of Images:  Represent an N x N image as N^2-dimensional vector. Calculate the Mean Facial Image:  Mean image is the average of all images.
  • Subtract the Mean Image from Sample images:  Each Face images differs from the average face image by a vector. Calculation of Eigenfaces:  Let’s assume a face image is of size 128 x 128.  After transforming the image into N^2 dimension, it becomes a vector of dimension 16384.  Task is to describe these images in low dimensional subspace i.e. Data Reduction.  Calculate Covariance Matrix which consists of all image vectors.
  •  Size of the covariance matrix (C=A AT ) is N^2 x N^2.  Computationally not feasible to calculate.  Construct a matrix L = ATA of size M x M where M = Number of Training Images.  Eigenvectors of “L” are calculated.  From these M eigenvectors, only P are chosen according the criterion.  These P Eigernvectors are called Eigenfaces. A= [Ф1 Ф2 …… ФM ]
  • Calculation ofWeightVectors:  After transformation of the images into eigenfaces,the weight vectors are formed of both Training and Test Images. Calculate the Euclidean Distance:  The Euclidean distance between two weight vectors provides a difference between the two images i and j. d( i, j) = || i - jΩ Ω Ω Ω ||2 where ω = weight, μ = eigenvector, Γ = new input image, Ψ = mean face
  • Implementation of Face RecognitionImplementation of Face Recognition (PCA) in MATLAB and Results(PCA) in MATLAB and Results  Face Databases: • Yale Face Database • Real-Time Face Database  Implementation in MATLAB: • Acquisition Module • PCA module • Recognition Module • Classification Module
  • Simulation 1Simulation 1 Testing and Results onYale Face Database
  • Recognition Rate using Different NumberRecognition Rate using Different Number of Training and Test Imagesof Training and Test Images Number of Training Images Per Individual Number of Testing Images Per Individual Recognition Rate (%) 1 10 15.00 2 9 25.00 3 8 40.00 4 7 62.00 5 6 71.00 6 5 70.00 7 4 75.00 8 3 77.00 9 2 86.60 10 1 86.60
  • Simulation 2Simulation 2 Testing and Results on Real-Time Face Database
  • Camera for Image AcquisitionCamera for Image Acquisition  Logitech 2 MP HD Webcam C600 Brand/Model Logitech C600 Technology RightLight Technology Sensor True 2-Mega pixel Resolution 1600 x 1200 Frames per Second Upto 30 fps Others USB 2.0 , Fixed focus
  • Face Detection LibraryFace Detection Library  Face Detection library created by W. Kienzle, G. Bakir, M. Franz and B. Scholkopf is used.  It is non-commercial Dynamic-link library.  The MATLAB version is used which implements a single function.  The method of reduced state vectors machines to increase the accuracy of Face Detection is used.  It detects the faces by adjusting the threshold.  The Image must be uint8 grayscale.  The Red Rectangle shows the face detected.  Library has the capability to detect all the faces in the image.
  •  The program is created to crop the detected face.  All the Training Images of six students taken at Bahria University are captured with their faces detected using this library.  Each image is of size 128 x 128.  These 60 images form a Real-time face Database.
  •  The complete Real-time Face Database of 6 students (10 images per Individual).
  • Recognition Rate using Different NumberRecognition Rate using Different Number of Training and Test Imagesof Training and Test Images Number of Training Images Per Individual Number of Testing Images Per Individual Recognition Rate (%) 1 9 64.0 2 8 68.3 3 7 74.0 4 6 77.0 5 5 79.7 6 4 87.5 7 3 87.5 8 2 91.2 9 1 95.0
  • Integration of RFID and FaceIntegration of RFID and Face RecognitionRecognition  Key-less System  Start of Attendance  Start Time of Class  Student Attendance  Security  Student Entry Time  End of Attendance  End Time of Class  Student Face Verification  Student Face Verification Record
  • Conclusion and Future workConclusion and Future work  Attendance system using RFID and Face Recognition is a very unique application.  Attendance and access control systems are common in most of the modern offices.  But they don’t consider the fact that RFID can be very insecure if some illegal person gets access to the RFID card.  Integration with face recognition system solves this problem.  A student is identified by RFID card and is verified by the Face Recognition system.
  •  The application can be enhanced to take the attendance of all university students .  With the use of multiple RFID and Face Recognition systems for different classrooms.  To handle a large database, a Database management system would be required.  It can easily be integrated in MATLAB using the Database Toolbox.