Recognition of Partially Occluded Face Using Gradientface and Local Binary Patterns

George D. C. Cavalcanti

Tsang Ing Ren,

Josivan R. Reis

2012 IEEE International Conference on Systems, Man, and Cybernetics

October 14-17, 2012, COEX, Seoul, Korea

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Outline

Introduction Occlusion Detection Recognition Experiments and results Conclusion

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INTRODUCTION

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Introduction

ه Challenges of face recognition systems is the problem of occlusion.

ه Uncontrolled environments such as drastic change of lighting, change of expression, beards and occlusions.

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Proposed approach

ه Apply in the problem for face recognition with sunglasses and scarf occlusion.

ه Consider illumination, rotation and inclination problems.

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Flowchart

Occlusion

Non-occlusion

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OCCLUSION DETECTION

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ه The image is divided into equal parts that are classified into occluded and non-occluded using MultiLayer Perceptron (MLP)

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ه Occlusion detection is a classification problem

N: a set of training imagesX: input layerY: output layer

Indicate : 1: non-occluded-1: occlude

Partial Face Classifier Using LDA and MLP”. In Proceedings of the 2010. IEEE/ACIS 9th International Conference on Computer and Information Science (ICIS '10)

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RECOGNITION

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ه For the recognition, Local binary

Pattern(LBP) feature is used on the non-occluded image part.

ه LBP widely used in face recognitionه Discriminative powerه Computational simplicityه Robustness to changes in grayscale.

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But:

LBP is not efficient for drastic lighting variations.

Solve:↓

we use a Gradientface as a preprocessing step before LBP.

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GradientFace

ه It is insensitive to variations in illumination and stands out in face recognition applications.

ه Gradientface Method:1. Transforms to the gradient domain

2. Eliminate noise or shadow (Gaussian filter)

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ه Smooth the image through a convolution with a Gaussian function∗ is the convolution operator

σ is the Gaussian function

ه Compute the image gradient I convolving in directions x, y

ه Generate as result ,Gradientface

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Local binary patterns

ln : Corresponds to the central pixel value lc : The 8-neigbor pixels values

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Recognition

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ه [Gradientface + LBP ] computational simplicity

robust to scales changes and illumination variations.

ه Define the similarity between the LBP histograms of each image a similarity distance is used [7].

[E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell, “Distance metric learning with application to clustering with side information,” in Advances in Neural Information Processing Systems 15 , S. Becker, S.Thrun, and K. Obermayer, Eds. Cambridge, MA: MIT Press, 2003, pp. 505–512 ]

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EXPERIMENTS AND RESULTS

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Database

ه AR Faceه More than 4000 color images (70 men 56 women)

ه With different facial expressions, lighting conditions and occlusions (sun glasses and scarf)

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ه ORLه 40 subjects, 10 different images for each subject.ه Facial expressions (open or closed eyes)ه Facial details (glasses and without glasses)

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Experiments1- MLPClassifier

ه Occlusion detection

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Experiments2- Recognition

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Database: AR Face

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Experiments2- Recognition

Database: ORL Face

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CONCLUSION

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Conclusion

ه Address the problem of face recognition with occlusion caused by sunglasses and scarf.

ه The Gradientface applied to image with illumination problem and used to pre-processing the image, improved the recognition.

ه Combination of pre-processing techniques and classifies can still demonstrate improvements in face recognition problems.

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Q&A

END

Thanks