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ATTENDENCE SYSTEM USING FACE RECOGNITION Automated Smart Attendance System Using Face Recognition

In The Human Body, The Face Is The Most Crucial Factor In Identifying Each Person As It Contains Many Vital Details. There Are Different Prevailing Methods To Capture Person's Presence Like Biometrics To Take Attendance Which Is A Time-consuming Process. This Paper Develops A Model To Classify Each Character's Face From A Captured Image Using A Collection Of Rules I.e., LBP Algorithm To Record The Student Attendance. LBP (Local Binary Pattern) Is One Among The Methods And Is Popular As Well As Effective Technique Used For The Image Representation And Classification And It Was Chosen For Its Robustness To Pose And Illumination Shifts. The Proposed ASAS (Automated Smart Attendance System) Will Capture The Image And Will Be Compared To The Image Stored In The Database. The Database Is Updated Upon The Enrolment Of The Student Using An Automation Process That Also Includes Name And Rolls Number. ASAS Marks Individual Attendance, If The Captured Image Matches The Image In The Database I.e., If Both Images Are Identical. The Proposed Algorithm Reduces Effort And Captures Day-to-day Actions Of Managing Each Student And Also Makes It Simple To Mark The Presence. Keywords—face Detection, Face Recognition, Image Processing, Local Binary Pattern

IMAGE FUSION TECHNIQUE A New Multi-Focus Image Fusion Techniquefor An Efficient Surveillance

Medical Image Fusion Is The Process Of Combining Images From Different Modalities To Make The Fused Image More Informative Than Any Of The Source Images. Images Of Different Modalities Include PET, CT, SPECT And MRI.Medical Image Fusion Can Combine Information From Multi-modality Images And Express Them Through A Single Image. How To Design A Fusion Method To Preserve More Information Becomes A Hot Topic. In This Paper, We Propose A Novel Multi-modality Medical Image Fusion Method Based On Synchronized-Anisotropic Diffusion Equation (S-ADE). First, The Modified S-ADE Model Which Is More Suitable For Magnetic Resonance Imaging (MRI) And Computed Tomography (CT) Images Is Employed To Decompose Two Source Images. We Get The Base Layers And Texture Layers. Next, The “Maximum Absolute Value” Rule Is Used For Base Layers Fusion. One Input Image Will Have High Spatial Resolution And Low Spectral Information And Another Image Will Have High Spectral Resolution And Vice Versa. The Aim Of Medical Image Fusion Is To Have A Single Image Having Both Spatial And Spectral Resolution. Most Commonly Used Transform Domain Methods Like Curvelet Transform Is Applied To Extract More Specific Information From The Source Images. Experimental Results Demonstrate That Fused Image Will Have Sharpened Image Resolution And The Fusion Performance Is Evaluated With Image Quality Assessment Metrics.

Smart ATM Using Face Verification Enhanced Security Feature Of ATMs Through Facial Recognition

Automated Teller Machines Also Known As ATM’s Are Widely Used Nowadays By Each And Everyone. The ATM Machine (Automated Teller Machine) Is An Electronic Device That Is Used By The Banks To Perform Banking Tasks Like Withdrawal Of Money, Transferring Of Money, And Many To Get Many Information About A User's Bank Account Without The Need To Visit A Bank. This System Revolutionised The Way Of Transactions. There Were No Long Lines Of Queue In Front Of The Bank For A Simple Withdrawal Of Money. The Number Of ATM’s A Bank Has Can Be A Factor In Considering The Strength Of A Bank. As There Is Increase In The Number Of ATM’s, There Is Also Increase In The Fraudulent Activities In The ATM. The Main Motivation Of This Project Is To Increase The Security Feature Of The Use Of ATM. The Current Method Uses Static Key (PIN) For Security. The Proposed Method Uses Face-id As A Key Incorporated With Current Method. The Advantages Can Be Found As That The Face-id Is Unique For Everybody; It Cannot Be Used By Anybody Other Than The User. For The Implementation Of The Face-id Scan, The Machine Learning And Image Processing Algorithms (Eigenface Algorithm) Are Used

SMART DOOR LOCK SYSTEM Face Recognition For Smart Door Lock System Using Hierarchical Network

Face Recognition System Is Broadly Used For Human Identification Because Of Its Capacity To Measure The Facial Points And Recognize The Identity In An Unobtrusive Way. The Application Of Face Recognition Systems Can Be Applied To Surveillance At Home, Workplaces, And Campuses, Accordingly. The Problem With Existing Face Recognition Systems Is That They Either Rely On The Facial Key Points And Landmarks Or The Face Embeddings From FaceNet For The Recognition Process. In This Paper, We Propose A Hierarchical Network (HN) Framework Which Uses Pre-trained Architecture For Recognizing Faces Followed By The Validation From Face Embeddings Using FaceNet. We Also Designed A Real-time Face Recognition Security Door Lock System Connected With Raspberry Pi As An Implication Of The Proposed Method. The Evaluation Of The Proposed Work Has Been Conducted On The Dataset Collected From 12 Students From Faculty Of Engineering And Technology, University Of Sindh. The Experimental Results Show That The Proposed Method Achieves Better Results Over Existing Works. We Also Carried Out A Comparison On Random Faces Acquired From The Internet To Perform Face Recognition And Results Shows That The Proposed HN Framework Is Resilient To The Randomly Acquired Faces.

FACE AND FINGERPRINT RECOGNITION VOTING SYSTEM Online Smart Voting System Using Biometrics Based Facial And Fingerprint Detection On Image Processing And CNN

India Being A Democratic Country, Still Conducts Its Elections By Using Voting Machines, Which Involves High Cost And Manual Labor. Web-based System Enables Voter To Cast Their Votes From Anywhere In The World. Online Website Has A Prevented IP Address Generated By The Government Of India For Election Purpose. People Should Register The Name And Address In The Website. Election Commission Will Collect The Fingerprint And Face Image From The Voters. The Database Or Server Will Store The Images. When The Images Are Obtained On The Casting Day, It Will Be Compared With Database And Provides A Secured Voting On The Election Day. System Utilizes Faces And Fingerprints To Unlock The Voting System, Similar To The Mobile Phone Are Used. The Current System Requires The Physical Presence Of Voter, Which Is Inconvenient To Many Voters. The Process Consumes Less Time As Well. Using The Detection Of Face And Fingerprint Images, The Number Of Fake Voters Can Be Reduced. The Eyes And Eyebrows Distance Remains Constant With Growing Age To Make The System More Secure. This Research Work Utilizes Ten Print Image To Detect The Correct Name Of Voter.

FACE RECOGNITION BASED ATTENDENCE SYSTEM Automated Smart Attendance System Using Face Recognition

In The Human Body, The Face Is The Most Crucial Factor In Identifying Each Person As It Contains Many Vital Details. There Are Different Prevailing Methods To Capture Person's Presence Like Biometrics To Take Attendance Which Is A Time-consuming Process. This Paper Develops A Model To Classify Each Character's Face From A Captured Image Using A Collection Of Rules I.e., LBP Algorithm To Record The Student Attendance. LBP (Local Binary Pattern) Is One Among The Methods And Is Popular As Well As Effective Technique Used For The Image Representation And Classification And It Was Chosen For Its Robustness To Pose And Illumination Shifts. The Proposed ASAS (Automated Smart Attendance System) Will Capture The Image And Will Be Compared To The Image Stored In The Database. The Database Is Updated Upon The Enrolment Of The Student Using An Automation Process That Also Includes Name And Rolls Number. ASAS Marks Individual Attendance, If The Captured Image Matches The Image In The Database I.e., If Both Images Are Identical. The Proposed Algorithm Reduces Effort And Captures Day-to-day Actions Of Managing Each Student And Also Makes It Simple To Mark The Presence. Keywords—face Detection, Face Recognition, Image Processing, Local Binary Pattern

FINGER PRINT RECOGNITION Biometric Identification And Verification System

Conventionally, The Identification Of Users Is Performed Using Passwords And PIN Numbers. However, Due To The Rapid Changes In Technology, These Security Measure's Abuse And Theft Are Also Increasing. This Led To The Birth Of The Biometric Security System, In Which The Identity Verification Of Individuals Is Performed Based On The Feature Vectors, Which Are Derived From The Physiological And/or Behavioral Characteristics Of Humans. The Categorization Of A Biometric System Is Done As Per The Number Of Traits Utilization For Authentication Purposes. There Are Various Available Methods For Identification Such As Face Recognition, Iris Recognition, Voice Recognition, Fingerprint Recognition, Etc. As Per Advancement In Technology, Information Security Becomes A Major Challenge Of The IT Industry. Thus, Authentication Plays An Important Role To Deals With Security. The Key Objective Of This Scheme Is To Ensure That The Rendered Services Are Accessible Only By The Authorized User And Not By An Illegal Person. Using Biometrics, An Individual's Identity Can Be Confirmed Or Established. In This Paper, A Comparative Survey Of Uni And The Multi-modal Biometric System Is Presented, And Analysis Is Performed To Know The Best System. The Purpose Is To Analyze The Latest Technology Based On The Progress Made And Unresolved Issues. A General Overview Of The Biometric Recognition System Is Presented, Along With The Performance, Parameters Is Presented. In Addition To The Single-modal System, This Article Also Discusses The Multi-modal Biometric System And Its Traditional Approaches Along With The Information Fusion Level. This Article Highlights The Potential, Market Value, And Prospects Of Biometric Technology.

HAND GESTURE RECOGNITION Automated Hand Gesture Recognition Using A Deep Convolutional Neural Network Model

The Tremendous Growth In The Domain Of Deep Learning Has Helped In Achieving Breakthroughs In Computer Vision Applications Especially After Convolutional Neural Networks Coming Into The Picture. The Unique Architecture Of CNNs Allows It To Extract Relevant Information From The Input Images Without Any Hand-tuning. Today, With Such Powerful Models We Have Quite A Flexibility Build Technology That May Ameliorate Human Life. One Such Technique Can Be Used For Detecting And Understanding Various Human Gestures As It Would Make The Human-machine Communication Effective. This Could Make The Conventional Input Devices Like Touchscreens, Mouse Pad, And Keyboards Redundant. Also, It Is Considered As A Highly Secure Tech Compared To Other Devices. In This Paper, Hand Gesture Technology Along With Convolutional Neural Networks Has Been Discovered Followed By The Construction Of A Deep Convolutional Neural Network To Build A Hand Gesture Recognition Application.

Secure And Lightweight Biometric-Based Remote Patient Authentication Scheme For Home Healthcare Systems

Recently, The Home Healthcare System Has Emerged As One Of The Most Useful Technology For E-healthcare. Contrary To Classical Recording Methods Of Patient’s Medical Data, Which Are, Based On Paper Documents, Nowadays All This Sensitive Data Can Be Managed And Forwarded Through Digital Systems. These Make Possible For Both Patients And Healthcare Workers To Access Medical Data Or Receive Remote Medical Treatment Using Wireless Interfaces Whenever And Wherever. However, Simplifying Access To These Sensitive And Private Data Can Directly Put Patient’s Health And Life In Danger. In This Paper, We Propose A Secure And Lightweight Biometric-based Remote Patient Authentication Scheme Using Elliptic Curve Encryption Through Which Two Mobile Healthcare System Communication Parties Could Authenticate Each Other In Public Mobile Healthcare Environments. The Security And Performance Analysis Demonstrate That Our Proposal Achieves Better Security Than Other Concurrent Schemes, With Lower Storage, Communication And Computation Costs.

Teeth Recognition Based Person Identification System

A Model Of Teeth Recognition Has Been Proposed To Identify A Person. The Persons' Teeth Image Has Been Matched Against The Database Of Teeth Images. An Algorithm Has Been Developed To Recognize The Teeth Image By Using The Image Processing Methods. The Author Has Been Set A Similarity Criterion To Match The Teeth Image Against The Image Database By Specifying The Threshold Value. This Similarity Criterion Is Used To Identify A Person And Person Details. The Labial Teeth And Gingiva Photographic Image Database (LTG-IDB) Have Been Taken To Verify The Results

Deep Representation Based Feature Extraction And Recovering For Finger-Vein Verification

Finger-vein Biometrics Has Been Extensively Investigated For Personal Verification. Despite Recent Advances In Fingervein Verification, Current Solutions Completely Depend On Domain Knowledge And Still Lack The Robustness To Extract Finger-vein Features From Raw Images. This Paper Proposes A Deep Learning Model To Extract And Recover Vein Features Using Limited A Priori Knowledge. Firstly, Based On A Combination Of Known State Of The Art Handcrafted Finger-vein Image Segmentation Techniques, We Automatically Identify Two Regions: A Clear Region With High Separability Between Finger-vein Patterns And Background, And An Ambiguous Region With Low Separability Between Them. The First Is Associated With Pixels On Which All The Segmentation Techniques Above Assign The Same Segmentation Label (either Foreground Or Background), While The Second Corresponds To All The Remaining Pixels. This Scheme Is Used To Automatically Discard The Ambiguous Region And To Label The Pixels Of The Clear Region As Foreground Or Background. A Training Dataset Is Constructed Based On The Patches Centered On The Labeled Pixels. Secondly, A Convolutional Neural Network (CNN) Is Trained On The Resulting Dataset To Predict The Probability Of Each Pixel Of Being Foreground (i.e. Vein Pixel) Given A Patch Centered On It. The CNN Learns What A Fingervein Pattern Is By Learning The Difference Between Vein Patterns And Background Ones. The Pixels In Any Region Of A Test Image Can Then Be Classified Effectively. Thirdly, We Propose Another New And Original Contribution By Developing And Investigating A Fully Convolutional Network (FCN) To Recover Missing Fingervein Patterns In The Segmented Image. The Experimental Results On Two Public Finger-vein Databases Show A Significant Improvement In Terms Of Finger-vein Verification Accuracy.

Automated Algorithm For Person Identification Through IRIS Recognition

In This Paper We Propose A New Biometric-based Iris Feature Extraction System. The System Automatically Acquires The Biometric Data In Numerical Format (Iris Images) By Using A Set Of Properly Located Sensors. We Are Considering Camera As A High Quality Sensor. Iris Images Are Typically Color Images That Are Processed To Gray Scale Images. Then The Feature Extraction Algorithm Is Used To Detect “IRIS Effective Region (IER)” And Then Extract Features From “IRIS Effective Region (IER)” That Are Numerical Characterization Of The Underlying Biometrics. Later On This Work Will Be Helping To Identify An Individual By Comparing The Feature Obtained From The Feature Extraction Algorithm With The Previously Stored Feature By Producing A Similarity Score. This Score Will Be Indicating The Degree Of Similarity Between A Pair Of Biometrics Data Under Consideration. Depending On Degree Of Similarity, Individual Can Be Identified. Authentication Is Also A Major Concern Area Of This Thesis. By Considering Biological Characteristics Of IRIS Pattern We Use Statistical Correlation Coefficient For This ‘IRIS Pattern’ Recognition Where Statistical Estimation Theory Can Play A Big Role.

Face Recognition Based Door Access System

The Security Currently Become A Very Important Issue In Public Or Private Institutions In Which Various Security Systems Have Been Proposed And Developed For Some Crucial Processes Such As Person Identifications, Verification Or Recognition Especially For Building Access Control, Suspect Identifications By The Police, Driver Licenses And Many Others. Face Recognitions Have Been An Active Area Of Research With Numerous Applications Since Late 1980s And Become One Of The Important Elements In Security System Development. This Paper Focuses On The Study And Development On An Automated Face Recognition System With The Potential Application For Office Door Access Control. The Technique Of Eigenfaces Based On The Principle Component Analysis (peA) And Artificial Neural Networks Have Been Applied Into The System. The Study Includes The Analysis Of The Influences Of Three Main Factors Of Face Recognition Namely Illumination, Distance And Subject's Head Orientation On The Developed Face Recognition System Purposely Built For Office Door Access Control. The Experimental Results Have Shown That The Developed System Has Achieved Good Performance Of Face Recognition Rate Of 80% At The Distance Of Camera And Subject Between 40 Cm To 60 Cm And The Subject's Orientation Head Angle Must Be Within The Range Of -20 To +20 Degrees.

Teeth Recognition Based Person Verification And Voting System

A Model Of Teeth Recognition Has Been Proposed To Identify A Person. The Persons' Teeth Image Have Been Matched Against The Database Of Teeth Images. An Algorithm Has Been Developed To Recognize The Teeth Image By Using The Image Processing Methods. The Author Has Been Set A Similarity Criterion To Match The Teeth Image Against The Image Database By Specifying The Threshold Value. This Similarity Criterion Is Used To Identify A Person And Person Details. The Labial Teeth And Gingiva Photographic Image Database (LTG-IDB) Have Been Taken To Verify The Results. After That, Voting Process Will Access Only Authorized User, Others Are Not Allowed.

Face-Recognition Based Attendance Management

In The Human Body, The Face Is The Most Crucial Factor In Identifying Each Person As It Contains Many Vital Details. There Are Different Prevailing Methods To Capture Person's Presence Like Biometrics To Take Attendance Which Is A Time-consuming Process. This Paper Develops A Model To Classify Each Character's Face From A Captured Image Using A Collection Of Rules I.e., LBP Algorithm To Record The Student Attendance. LBP (Local Binary Pattern) Is One Among The Methods And Is Popular As Well As Effective Technique Used For The Image Representation And Classification And It Was Chosen For Its Robustness To Pose And Illumination Shifts. The Proposed ASAS (Automated Smart Attendance System) Will Capture The Image And Will Be Compared To The Image Stored In The Database. The Database Is Updated Upon The Enrolment Of The Student Using An Automation Process That Also Includes Name And Rolls Number. ASAS Marks Individual Attendance, If The Captured Image Matches The Image In The Database I.e., If Both Images Are Identical. The Proposed Algorithm Reduces Effort And Captures Day-to-day Actions Of Managing Each Student And Also Makes It Simple To Mark The Presence. Keywords—face Detection, Face Recognition, Image Processing, Local Binary Pattern

Local Binary Pattern Histogram And HAAR Cascading Technique Based Face Recognition

The Attendance System Is Used To Track And Monitor Whether A Student Attends A Class. There Are Different Types Of Attendance Systems Like Biometric-based, Radiofrequency Cardbased, Face Recognition Based And Old Paper-based Attendance System. Out Of Them All, A Face Recognition Based Attendance System Is More Secure And Time-saving. There Are Several Research Papers Focusing On Only The Recognition Rate Of Students. This Research Focusing On A Face Recognition Based Attendance System With Getting A Less False-positive Rate Using A Threshold To Confidence I.e. Euclidean Distance Value While Detecting Unknown Persons And Save Their Images. Compare To Other Euclidean Distance-based Algorithms Like Eigenfaces And Fisherfaces, Local Binary Pattern Histogram (LBPH) Algorithm Is Better. We Used Haar Cascade For Face Detection Because Of Their Robustness And LBPH Algorithm For Face Recognition. It Is Robust Against Monotonic Grayscale Transformations. Scenarios Such As Face Recognition Rate, False-positive Rate For That And False-positive Rate With And Without Using A Threshold In Detecting Unknown Persons Are Considered To Evaluate Our System. We Got Face Recognition Rate Of Students Is 77% And Its False-positive Rate Is 28%. This System Is Recognizing Students Even When Students Are Wearing Glasses Or Grown A Beard. Face Recognition Of Unknown Persons Is Nearly 60% For Both With And Without Applying Threshold Value. Its False-positive Rate Is 14% And 30% With And Without Applying Threshold Respectively

Face Recognition Using Local Binary Pattern Histogram And HAAR Cascading Technique

The Attendance System Is Used To Track And Monitor Whether A Student Attends A Class. There Are Different Types Of Attendance Systems Like Biometric-based, Radiofrequency Cardbased, Face Recognition Based And Old Paper-based Attendance System. Out Of Them All, A Face Recognition Based Attendance System Is More Secure And Time-saving. There Are Several Research Papers Focusing On Only The Recognition Rate Of Students. This Research Focusing On A Face Recognition Based Attendance System With Getting A Less False-positive Rate Using A Threshold To Confidence I.e. Euclidean Distance Value While Detecting Unknown Persons And Save Their Images. Compare To Other Euclidean Distance-based Algorithms Like Eigenfaces And Fisherfaces, Local Binary Pattern Histogram (LBPH) Algorithm Is Better. We Used Haar Cascade For Face Detection Because Of Their Robustness And LBPH Algorithm For Face Recognition. It Is Robust Against Monotonic Grayscale Transformations. Scenarios Such As Face Recognition Rate, False-positive Rate For That And False-positive Rate With And Without Using A Threshold In Detecting Unknown Persons Are Considered To Evaluate Our System. We Got Face Recognition Rate Of Students Is 77% And Its False-positive Rate Is 28%. This System Is Recognizing Students Even When Students Are Wearing Glasses Or Grown A Beard. Face Recognition Of Unknown Persons Is Nearly 60% For Both With And Without Applying Threshold Value. Its False-positive Rate Is 14% And 30% With And Without Applying Threshold Respectively

Face Recognition With Attendance Management System

In The Human Body, The Face Is The Most Crucial Factor In Identifying Each Person As It Contains Many Vital Details. There Are Different Prevailing Methods To Capture Person's Presence Like Biometrics To Take Attendance Which Is A Time-consuming Process. This Paper Develops A Model To Classify Each Character's Face From A Captured Image Using A Collection Of Rules I.e., LBP Algorithm To Record The Student Attendance. LBP (Local Binary Pattern) Is One Among The Methods And Is Popular As Well As Effective Technique Used For The Image Representation And Classification And It Was Chosen For Its Robustness To Pose And Illumination Shifts. The Proposed ASAS (Automated Smart Attendance System) Will Capture The Image And Will Be Compared To The Image Stored In The Database. The Database Is Updated Upon The Enrolment Of The Student Using An Automation Process That Also Includes Name And Rolls Number. ASAS Marks Individual Attendance, If The Captured Image Matches The Image In The Database I.e., If Both Images Are Identical. The Proposed Algorithm Reduces Effort And Captures Day-to-day Actions Of Managing Each Student And Also Makes It Simple To Mark The Presence. Keywords—face Detection, Face Recognition, Image Processing, Local Binary Pattern

Real Time Face Recognition System Using Neural Network

Face Detection Is A Necessary First-step In Face Recognition Systems, With The Purpose Of Localizing And Extracting The Face Region From The Background. The Self- Organizing Map (SOM) Neural Network Has Been Used For Training Of Database And Simulation Of FR System. The Developed Algorithm For The Face Recognition System Formulates An Image-based Approach, Using Directional Discrete Cosine Transform (DDCT), Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) And Sobel Edge Detection, Simulated In MATLAB. Simulation Results Are Very Promising.

Image Feature Extraction Techniques And Their Applications For CBIR And Biometrics Systems

Content Based Image Retrieval (CBIR) Becomes A Very Challenging Taks Due To The Rapid Growth In Multimedia Content And Its Visual Complexity. From Query By Image To Retrieval Of Relevant Images, CBIR Has Different Phases. However, Features Extraction Of Images Is One Of The Important Phases. Recently Convolutional Neural Network (CNN) Shows Good Results In The Field Of Computer Vision Due To The Ability Of Extraction Features From The Images. This Paper Introduces CNN For Features Extraction From Images, In CBIR System. Euclidean Distance Is Used For Association Among Query And Stored Images Using The Extracted Features. Performance Of The Proposed Work Is Evaluated Using Precision. The Proposed Work Shows Improved Results As Compared To The Existing Works.

Image Quality Assessment For Fake Biometric Detection Application To Iris Fingerprint And Face Recognition

The Increasing Interest In The Evaluation Of Biometric Systems Security Is An Important Issue To Be Considered. The Different Threats Called Direct Or Spoofing Attacks Where In These Attacks, The Intruder Uses Some Type Of Synthetically Produced Artifact (e.g., Gummy Finger, Printed Iris Image Or Face Mask), Or Tries To Mimic The Behavior Of The Genuine User , To Fraudulent Access Of The Biometric System Have Motivated To New Efficient Protection Measures. In This Paper, We Present A Novel Software-based Fake Biometric Detection Method That Can Be Used In Multiple Biometric Systems To Detect Different Types Of Fraudulent Access Attempts. The Use Of Image Quality Assessment For Liveness Detection Is Motivated By The Assumption That: It Is Expected That A Fake Image Captured In An Attack Attempt Will Have Different Quality Than A Real Sample Acquired In The Normal Operation Scenario For Which The Sensor Was Designed. The Proposed Approach Presents A Very Low Degree Of Complexity, Which Makes It Suitable For Real-time Applications, Using General Image Quality Features Extracted From One Image To Differentiate Between Real And Fake Samples. This Proposed Work Enhances The Security Of Biometric Recognitions, By Using The Liveness Detection Through Image Quality Assessment And By Fusion Of Multiple Biometric Traits. The SVM Classifier Is Used For Differentiating Between The Real And Fake Samples.

Image Set Based Collaborative Representation For Face Recognition

Real Time Face Recognition Is Challenging Due To Time Taken For Searching The Test Image Within A Wide Variation Of Training Images. We Propose An Efficient Face Recognition Method By Applying Collaborative Representation Based Classification (CRC) Technique In Two Steps. Using CRC First We Select The Images Closer To The Pose Of The Test Image Compare To Others In The Training Set. The Test Image Is Then Searched Among The Selected Training Images Only, Instead Of All, Thus Reducing The Search Space And Time. For Person Recognition We Calculate Gabor Wavelets Of The Eye Regions Of The Test And The Selected Training Images As The Basis Functions Containing Detail Edge Information. The Images Are Reconstructed As A Linear Span Of The Basis Vectors Using CRC And The Sparse Coefficient Vectors Are Used As Feature Vectors. To Obtain The Best Match Image We Apply

Finger Print Combination For Privacy Protection

We Propose Here A Novel System For Protecting Fingerprint Privacy By Combining Two Different Fingerprints Into A New Identity. In The Enrollment, Two Fingerprints Are Captured From Two Different Fingers. We Extract The Minutiae Positions From One Fingerprint, The Orientation From The Other Fingerprint, And The Reference Points From Both Fingerprints. Based On This Extracted Information And Our Proposed Coding Strategies, A Combined Minutiae Template Is Generated And Stored In A Database. In The Authentication, The System Requires Two Query Fingerprints From The Same Two Fingers Which Are Used In The Enrollment. A Two-stage Fingerprint Matching Process Is Proposed For Matching The Two Query Fingerprints Against A Combined Minutiae Template. By Storing The Combined Minutiae Template, The Complete Minutiae Feature Of A Single Fingerprint Will Not Be Compromised When The Database Is Stolen. Furthermore, Because Of The Similarity In Topology, It Is Difficult For The Attacker To Distinguish A Combined Minutiae Template From The Original Minutiae Templates. With The Help Of An Existing Fingerprint Reconstruction Approach, We Are Able To Convert The Combined Minutiae Template Into A Real-look Alike Combined Fingerprint. Thus, A New Virtual Identity Is Created For The Two Different Fingerprints, Which Can Be Matched Using Minutiae-based Fingerprint Matching Algorithms. The Experimental Results Show That Our System Can Achieve A Very Low Error Rate With FRR 0.4% At FAR 0.1%. Compared With The State-of-the-art Technique, Our Work Has The Advantage In Creating A Better New Virtual Identity When The Two Different Fingerprints Are Randomly Chosen.