End of Year Presentation

Principle Investigator: David Pilkington

Supervisor: James Connan

BUILDING A ROBUST FACIAL RECOGNITION SYSTEM BASED ON GENERIC TOOLS

CONTENTS • Introduction

• Recap

• Changes to image database

• Testing Framework

• Benchmark System

• Design

• Testing Results

• Weaknesses and Proposed Solutions

• Developed System

• Design

• Implementation and Encountered Problems

• Experimental Results

• Concluding Thoughts and Possible Future Work

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RECAP • Makes use of the EmguCV package (C# wrapper for OpenCV)

• Logitech 300 webcam (640 x 480)

• Benchmark System to establish baseline performance

• Identify weaknesses

• Propose and implement solutions

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CHANGES TO THE IMAGE DATABASE • Comprised of 50 images

• 10 individuals with 5 images each

• Captured using the tools provided by the package

• Stored as .png files due to lossless compression (640 x 480)

• Varying backgrounds, lighting conditions, sexes, race

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TESTING FRAMEWORK

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• Consists of 5 tests:

Test Description 1 1 training image 2 2 training images 3 3 training images 4 4 training images 5 5 training images

BENCHMARK SYSTEM

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DESIGN

Input Image EigenObjectRe

cogniser Identified

Face

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training

TEST RESULTS

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Test Recognition Rate 1 40% (s2, s3) 2 40% (s2, s3) 3 40% (s2, s3) 4 40% (s2, s3) 5 40% (s2, s3)

IDENTIFIED WEAKNESSES

• Lower recognition rate than we would like (40%)

• No ability to reject an image

• Highly afftected by spurious background noise

• Highly affected by varying lighting

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PROPOSED SOLUTION

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• Make use of face detection to crop image

• Make use of skin segmentation to further reduce background noise

• Use histogram equalisation to reduce light variability

• Introduce a threshold to the recongniser to allow the rejection of images

DEVELOPED SYSTEM

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DESIGN

Input Image Face Detector Skin Segmentation

Eigen Recogniser

Identified Face

Image Rejected

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training

FACE DETECTOR

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SKIN SEGMENTATION: DESIGN

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Input Image Nose Detector Colour Modelling Segmentation Segmented

Image

SKIN SEGMENTOR : NOSE IDENTIFICATION

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SKIN SEGMENTOR: SKIN MODELING

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SKIN SEGMENTOR: SEGMENTATION

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EIGEN RECOGNISER

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TRAINING

• Makes use of the eigenfaces technique which is base on PCA

• Receive training images and labels

• Create the PCA subspace

• Compute eigenvectors for training images

• Project the training images onto the PCA subspace to obtain eigenvalues for images

RECOGNITION

• Project test image onto the PCA subspace and obtain eigenvalues

• Find nearest neighbour by shortest Euclidean Distance

EXPERIMENTAL RESULTS

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Test Recognition Rate 1 60% (s1, s2, s4) 2 60% (s1, s2, s4) 3 60%(s1, s2, s4) 4 80% (s1, s2, s3, s4) 5 80% (s1, s2, s3, s4)

EXPERIMENTAL RESULTS : COMPARISON

Euclidean Distance to Nearest Neighbour

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EXPERIMENTAL RESULTS : COMPARISON

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VS

Benchmark System Developed System

EXPERIMENTAL RESULTS : COMPARISON

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Database Load Time

CONCLUDING THOUGHTS AND POSSIBLE FUTURE WORK

Conclusions • Background noise reduction has proved to be effective • Light sensitivity is still a problem • Thresholding was temperamental • Scalability problem due to image processes

Possible Future Extensions • Expand image data set • Comparison between recognition techniques • Effects of glasses, beards, etc. • Image verification

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