Labview-based Face Recognition In Attendance Tracking System

• Author(s):

  Shanmugam M | Magesh Kumar R | Nimal Dinesh M | Nitheesh Kumar R | Nithish kumar T

• Keywords:

  Face Recognition, Attendance Tracking, LabVIEW, Computer Vision, Real-Time Monitoring, Image Processing, ROI Extraction, Automated Logging, Pattern Matching, Intelligent Attendance System.

• Abstract:

  Institutional Contexts Have Long Relied On Traditional Attendance Systems That Involve Taking Attendance, Using Roll Call, Paper Sign-in Sheets, And Manual Record Keeping, Which Have All Been Seen As Inefficient. They Are Labor Intensive, Subject To Human Error And Can Be Subject To Proxy Attendance Which Adds An Unnecessary Strain On Administrators And Undermines The Integrity Of Attendance Information. This Paper Presents A Real-time, Contactless Face Recognition Attendance Tracking System Based On The LabVIEW Platform That Can Overcome These Issues By Implementing Intelligent Automation With Computer Vision And Image Processing. The System's Core Is A Common Webcam Which Is Constantly Taking Snapshots From The Face Surrounding The User. The Images Are Then Processed Through A Rigidly Designed Pre-processing Chain, Which Starts With The Conversion Of The Images To Gray Scale And Then To A Set Of Images That Have Been Normalized Using A Histogram. This Is To Compensate For Various Lighting Conditions, Followed By Filtering To Removes Image Noise, And Finally A Region-of-interest Extraction That Selects The Most Relevant Areas Of The Face For Further Processing. This Pre-processing Pipeline Is Implemented In The LabVIEW Vision Framework. This Pre-processing Is What Gives The System The Accuracy It Needs Despite Less Than Optimal Conditions. After Images Have Been Prepared, The LabVIEW Vision Development Module Assumes Control, Running The Algorithms Necessary For Face Detection, Feature Extraction And Template Matching Against A User Database Of Registered Faces. The System Automatically Records Attendance When A Face Is Successfully Recognized, Adds A Timestamp, And Provides A Confidence Score, All Of Which Are Shown In Real Time On LabVIEW's Interactive Graphical Interface. The Experimental Evaluation Was Done Under Lab Conditions, And The Results Were Positive; 98.2% Face Detection Accuracy, 96.9% Recognition Accuracy And 97.4% Positive Result For Attendance Logging. Most Importantly, These Figures Were Maintained Under Different Lighting Conditions And Moderate Changes In Head Orientation, Which Are Typical Situations For Which Traditional Recognition Systems Struggle. A Confidence Threshold Mechanism Provides An Extra Degree Of Reliability, Which Decreases The Chances Of False-positive Reports And Guarantees That Attendance Is Accurate. The Value Of This System Is That All Components Of The System, From Image Acquisition To Preprocessing, Image Recognition, Decision-making, And Logging, Are Integrated In A Single Cohesive Real-time Platform. No Need For Post Session Check Or Manual Correction. Administrators Always Have Visibility Of The Identification Status, Attendance Logs And System Performance Statistics On A User-friendly Front Panel, Which Allows Them To Stay Informed And Stay In Control. The Outcome Is A Scalable, Cost-effective, And Easy-to-solve Solution That Could Be Adopted In Smart Schools, Universities, And Organizations Where Accurate And Automated Attendance Tracking Is Not Just A Better Practice, It's A Must.

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Other Details

• Paper id:

  IJSARTV12I5105519

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-26

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