Automated Attendance Machine; FaceDetection and Recognition.

Presented by:

Kitili Jackson Mwendwa.F17/1437/2011

INTRODUCTIONTraditional methods of monitoring student attendances are tedious,time consuming and prone to inaccuracies as some students often signfor their absent colleagues.

This project proposes the design and implementation of an automatedattendance management system using face detection and recognition.

This biometric system will consist of the following processes:• Enrollment process• Identification/Verification

SYSTEM DESIGNThe attendance management system:• Desktop module• Mobile module

The Desktop Module system is divided in to two related sub-systems, the training set manager and the face recognizer.

Training set manager components.• Image acquisition component• Face detection component.

Face recognizer Sub system• Training component• Image acquisition• Face detection and recognition• Attendance component

Functions of the two sub systems.

Database of

Faces (This

contains the

training set)

Image Acquisition

(Gets the input

image with the

human face )

Face Recognizer

Recognizes the

detected faces from the

trained data

Trains the recognizer

on the training set

Loads the training set

Shows the calculated

average face and the

eigenfaces

Connects to the faces

database

TRAINING SET

MANAGER

Connects to faces

Database

Loads the training

set to display present

faces

Deletes a face from

the training set

Updates a face in the

training set

Adds faces with

labels to the training

set

Face Detector

Component

(detects faces and

extracts them)

SYSTEM DESIGN

Face detection

Viola jones object detection frame work based on Haar Cascades.

Improvements:-

• scale increase rates,• minimum detection scale,• Canny pruning flag and• Minimum neighbors threshold.

An input variable array for group photos

SYSTEM DESIGN

SYSTEM DESIGNFace Recognition

Principal Component Analysis(PCA) based Eigen faces method wasused.

PCA uses an orthogonal transformation to convert a set of correlatedM faces in to a set of K faces of uncorrelated variables calledprincipal components or eigen faces.

The weight of the incoming unknown image was found and thencompared to the weights of those already in the system. If the inputimage's weight is over a given threshold it is considered to beunknown.

Input image is identified by finding the image in the database whoseweights are the closest to the weights of the input image. Theimage in the database with the closest weight will be returned as ahit to the user of the system.

Face Recognition Algorithm flow chart

Imageprocessing

andcomparison

module

Database

Recognise the faces

Compares withDatabase and fills upthe attendance

Cont’d …

Since all faces of the same person are heterogeneous,several images of the same person were chosen withdifferent facial expressions and under differentillumination conditions.

Tools

Mobile tools• OpenCV for Android• Android studio/Eclipse IDE

Desktop tools• Development tools Visual studio, MS Access• EmguCV library• Programming language C#

RESULTSFace Detection• Single and group frontal photos had 100% face detection

rate• Live camera feed – track and recognize.

RESULTSFace Recognition

Yale and Local faces yielded an average percentagerecognition rate of about 80%.

First few eigen faces and average face obtained wereenough to fully represent all the faces in the trainingset – image compression.

RESULTS• The labels of the returned/recognized faces

were utilized in populating the databasefields.

FUTURE SCOPE Anti spoofing techniques.

Additional module for verification-foolproof.

Automated attendance report generation.

Well structured attendance registers for each class.

THANK YOU