The document presents a project on a smart attendance system utilizing facial recognition technology to automate and simplify attendance tracking for students. It outlines objectives, problem statements, proposed algorithms, and the system's capability to reduce manual errors in attendance recording. The conclusion emphasizes the potential real-world applications of the developed face recognition system in digital security.

1. MAANEESHA S -111620106057
KALLURU BHAVITHA – 11620106046
LAKSHMI PRIYA S-111620106051
NIVETHA S-111620106063
UNDER THE GUIDANCE OF
SIVALAKSHMI P
AP/ ECE
Smart attendance system by face RECOGNITION

2. CONTENTS
OBJECTIVE
INTRODUCTION
PROBLEM STATEMENT
LITERATURE REVIEW
PROPOSED WORK
 ALGORITHM
 FLOW DIAGRAM/BLOCK DIAGRAM
 MATHEMATICAL MODEL
SIMULATION TOOL
CONCLUSION
REFERENCES

3. ABSTRACT
FACIAL RECOGNITION IS A WAY OF IDENTIFYING OR
CONFIRMING AN INDIVIDUAL'S IDENTITY USING THEIR FACE.
FACIAL RECOGNITION SYSTEMS CAN BE USED TO IDENTIFY
PEOPLE IN PHOTOS, VIDEOS, OR IN REAL-TIME. FACIAL
RECOGNITION IS A CATEGORY OF BIOMETRIC SECURITY.
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4. OBJECTIVE
THE MAIN OBJECTIVE OF THIS PROJECT IS TO OFFER
SYSTEM THAT SIMPLIFY AND AUTOMATE THE PROCESS
OF RECORDING AND TRACKING STUDENT’S ATTENDANCE
THROUGH FACE RECOGNITION TECHNOLOGY.
 IT IS BIOMETRIC TECHNOLOGY TO IDENTIFY OR VERIFY A
PERSON FROM A DIGITAL IMAGE OR SURVEILLANCE
VIDEO.
IT GIVES SOLUTION TO THE FACULTY THEREBY REDUCING THE
BURDEN IN TAKING ATTENDANCE.
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5. PROBLEM STATEMENT
THE GOAL IS TO DESIGN AND IMPLEMENT SOFTWARE
SYSTEM FOR FACE RECOGNITION-BASED ATTENDANCE
SYSTEM FOR FRONTAL FACES USING STATIC IMAGES OR
THROUGH WEBCAM.
ATTENDANCE HAS BEEN TAKEN MANUALLY WHICH COULD
LED TO MANUAL ERROR.
THE SYSTEM SHOULD WORK ON AI PLATFORM.
IN THIS PROJECT WE ARE USING LOCAL BINARY PATTERN
ALGORITHM
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6. LITERATUREREVIEW
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Sl No Title of the Paper Year of
Publication
Methodology Pros Cons
1 Facial emotion
recognition
Wei-long zheng nad
bao-liang lu
2018 EEG-based
affective models
without labeled
data using transfer
learning
techniques
Positive
(80.01%)
neutral
(25.76%)
and negative
(10.24%)
Emotions are
often confused
with each other.
2 Facial emotion
recognition
Zixingzhang , Fabien
ringeval etc….
2017 Semi-supervised
learning (SSL)
technique
Delivers a
strong
performance
(UAR =
76.5%)
SSL methods
by atleast 5.0%.
3 Facial emotion
recognition Y.Fan ,
X.Lu ,D.Li and Y.Liu
2016 Video-based
emotion
recognition using
CNN-RNN and
C3D Hybrid
Networks
Achieved
accuracy
59.02%
Emotion
labeled video
clips in training
set which is
best till now.

7. PROPOSEDWORK
 THE SURVILLEANCE
CAMERA WILL
CAPTURE THE IMAGE
 IT RECOGNIZES THE
FACE OF THE PERSON
FROM DATA SETS
TRAINED WITH TIME
MENTIONED
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8. ALGORITHM FOR FACE RECOGNITION
 WE TRAIN THE MODEL USING VARIOUS FACES OF
STUDENTS IN A CLASS
THE SYSTEM CONVERT THE FACE DATA INTO
MATHEMATICAL DATA
AND STORE THE FACE DATA OF PERSONS TO THEIR
REGISTER NUMBER
IT STORES OUR FACE DATA USING 68 SPECIFIC POINTS.
THEN IT STORES ALL THE FACE DATA IN CSV FORMAT AND
SAVE THEM USING THEIR IDENTIFICATION NUMBER
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9. ALGORITHM FOR ATTENDANCE SYSTEM
WHEN THE PERSON IS AT RANGE OF MODEL
THE IDENTIFICATION PROCESS IS DONE USING MATHEMATICAL
MODEL OF FACE
WHEN THE MATHEMATICAL FACE MATCHED WITH ANY DATA. THEN
IT MAKES THE ATTENDANCE PRESENT ON PARTICULAR DAY.
IF THE FACE RECOGNITIONS FAILS. THE MODELASK THE PERSON
TO ENTER HIS/HER REGISTER NUMBER.
THEN ACCORDING TO REGISTER NUMBER THE SYSTEM TRIES TO
MERGE PRESENT FACE DATA WITH PREVIOUS FACE DATA TO OVER
COME THE PROBLEM OF NEXT TIME NOT RECOGNIZING IT.
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10. FLOW CHART FOR FACE RECOGNITION
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11. FLOW CHART FOR ATTENDANCE
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12. SIMULATIONTOOL
• VISUAL STUDIO
A VISUAL STUDIO CODE EXTENSION WITH RICH SUPPORT FOR THE PYTHON
LANGUAGE
• PYCHARM
PYCHARM PROVIDES SMART CODE COMPLETION, CODE
INSPECTIONS, ON-THE-FLY ERROR HIGHLIGHTING AND QUICK-
FIXES, ALONG WITH AUTOMATED CODE REFACTORING AND RICH
NAVIGATION CAPABILITIES.
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13. CONCLUSION
DEVELOPMENT OF A FACE RECOGNITION SYSTEM IMPLEMENTING
THE COMPUTER VISIONS AND ENHANCING THE ADVANCED
FEATURE EXTRACTION AND CLASSIFICATION .
IT FOCUS ON IMPROVING THE PERFORMANCE OF THE SYSTEM
AND DERIVING MORE APPROPRIATE CLASSIFICATIONS WHICH MAY
BE USEFUL IN REAL WORLD APPLICATIONS .
THIS SYSTEM CAN BE USED IN DIGITAL IN SECURITY SYSTEMS
WHICH CAN IDENTIFY A PERSON , IN ANY FORM OF EXPRESSION .
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14. THANK YOU
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