Multi-scale Local Binary Pattern Histogram for face recognition

Multi-scale Local Binary Pattern Histogram for
Face Recognition
Chi Ho CHAN
Submitted for the Degree of
Doctor of Philosophy
from the
University of Surrey
Centre for Vision, Speech and Signal Processing
School of Electronics and Physical Sciences
University of Surrey
Guildford, Surrey GU2 7XH, U.K.
September 2008
© Chi Ho CHAN 2008

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Sum mary
Recently, the research in face recognition has focused on developing a face representa
tion th at is capable of capturing the relevant information in a manner which is invariant
to facial expression and illumination. Motivated by a simple but powerful texture de
scriptor, called Local Binary Pattern (LBP), our proposed system extends this descrip
tor to evoke multiresolution and multispectral analysis for face recognition. The first
descriptor, namely Multi-scale Local Binary Pattern Histogram (MLBPH), provides a
robust system which is relatively insensitive to localisation errors because it benefits
from the multiresolution information captured from the regional histogram. The second
proposed descriptor, namely Multispectral Local Binary Pattern Histogram (MSLBP),
captures the mutual relationships between neighbours at pixel level from each spectral
channel. By measuring the spatial correlation between spectra, we expect to achieve
higher recognition rate. The resulting LBP methods provide input to LDA and various
classifier fusion methods for face recognition. These systems are implemented and com
pared with existing Local Binary Pattern face recognition systems and other state of
art systems on Feret, XM2VTS and FRGC 2.0 databases, giving very promising results
in the controlled environment.
Photometric normalisation is important for face recognition, even if illumination-robust
features, such as Gabor or LBP, are used for face representation. In order to study
the merits of photometric normalisation, five different photometric normalisation meth
ods have been investigated. A superior performance is achieved by MLBPH with the
Preprocessing Sequence method in all the tests. The results of a comparison with
the state-of-art systems show that the proposed Multi-scale Local Binary Pattern his
togram method with the Preprocessing Sequence photometric normalisation achieves
similar performance to the best performing systems, its key advantage is that it offers
a simple solution which is robust to localisation errors and changing illumination.
K ey w ords: Face Recognition, Local Binary Pattern, Photometric Normalisation.
Email: [e-mail]
WWW: http://www.eps.surrey.ac.uk/

A cknowledgem ents
I would like to take the opportunity to appreciate my supervisor Professor Josef Kittler
for his invaluable guidence throughout the project. I am also in debt to my second
supervisor Dr. Kerion Messer for his advice and help during the first year.
I am grateful to my colleagues who have been helping a lot with my research. Especially
I would like to thank to Dr. Xuan Zou, Dr. Norman Poh, Dr. Jose Rafael Tena, Dr.
Jean-Yves Guillemaut, Dr. Bill Christmas, Budhaditya Goswami, Omolara Fatukasi,
Dr. James Short, Simon Ennis, Dr. Lee Gregory for their assistances, discussions and
suggestions.
Finally, I would like to thank my girlfriend Hu HU, my parents, my family members
and my friends for all their support and encouragement.

C ontents
1 Introduction 1
1.1 Face Recognition System ................................................................................. 1
1.2 Challenges of Face R ecognitio n .............................................................. 4
1.3 C ontributions .................................................................................................... 6
1.4 Overview of T h e s is ......................... 8
2 Overview of Face R ecognition 11
2.1 Generic Face R ec o g n itio n .............................................................................. 13
2.1.1 Geometric Normalisation .................................................................. 16
2.1.2 Photometric N orm alisatio n ............................................ 17
2.1.3 Gabor wavelets .................................................................................. 17
2.1.4 Feature Selection ..................................................... 18
2.1.5 Feature E x trac tio n ............................................................................... 21
2.1.6 Classifier .............................................................................................. 26
2.2 S u m m ary ................................................................................. 28
3 Ordinal m easures for Face representation 31
3.1 Ordinal Contrast Encoding ........................................................................ 32
3.2 Structured Ordinal Contrast E nco d ing .......................................................... 35
3.2.1 Quadrant Bit Coding ......................................................................... 35
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