29-04-2014, 04:46 PM
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions
[attachment=62669]
Abstract
Recognition in uncontrolled situations is one of the most important
bottlenecks for practical face recognition systems. We address this by combining
the strengths of robust illumination normalization, local texture based face repre-
sentations and distance transform based matching metrics. Specifically, we make
three main contributions: (i) we present a simple and efficient preprocessing chain
that eliminates most of the effects of changing illumination while still preserving
the essential appearance details that are needed for recognition; (ii) we introduce
Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP)
local texture descriptor that is more discriminant and less sensitive to noise in
uniform regions; and (iii) we show that replacing local histogramming with a lo-
cal distance transform based similarity metric further improves the performance
of LBP/LTP based face recognition. The resulting method gives state-of-the-art
performance on three popular datasets chosen to test recognition under difficult
illumination conditions: Face Recognition Grand Challenge version 1 experiment
4, Extended Yale-B, and CMU PIE.
Introduction
One of the key challenges of face recognition is finding efficient and discriminative fa-
cial appearance descriptors that can counteract large variations in illumination, pose,
facial expression, ageing, partial occlusions and other changes [27]. There are two
main approaches: geometric feature-based descriptors and appearance-based descrip-
tors. Geometric descriptors can be hard to extract reliably under variations in facial
appearance, while appearance-based ones such as eigenfaces tend to blur out small de-
tails owing to residual spatial registration errors. Recently, representations based on
local pooling of local appearance descriptors have drawn increasing attention because
they can capture small appearance details in the descriptors while remaining resistant
to registration errors owing to local pooling. Another motivation is the observation that
human visual perception is well-adapted to extracting and pooling local structural in-
formation (‘micro-patterns’) from images [2]. Methods in this category include Gabor
wavelets [16], local autocorrelation filters [11], and Local Binary Patterns [1].
Related Work
As emphasized by the recent FRVT and FRGC trials [19], illumination variations are
one of the most important bottlenecks for practical face recognition systems. Gener-
ally, one can cope with this in two ways. The first uses training examples to learn a
global model of the possible illumination variations, for example a linear subspace or
manifold model, which then generalizes to the variations seen in new images [5,3]. The
disadvantage is that many training images are required.
The second approach seeks conventional image processing transformations that re-
duce the image to a more “canonical” form in which the variations are suppressed. This
has the merit of easy application to real images and the lack of a need for comprehensive
training data. Given that complete illumination invariants do not exist [7], one must con-
tent oneself with finding representations that are resistant to the most common classes
of natural illumination variations. Most methods exploit the fact that these are typically
characterized by relatively low spatial frequencies. For example, the Multiscale Retinex
(MSR) method of Jobson et al. [13] normalizes the illumination by dividing the image
by a smoothed version of itself.
Local Ternary Patterns (LTP)
LBP’s are resistant to lighting effects in the sense that they are invariant to monotonic
gray-level transformations, and they have been shown to have high discriminative power
for texture classification [17]. However because they threshold at exactly the value of
the central pixel ic they tend to be sensitive to noise, especially in near-uniform image
regions. Given that many facial regions are relatively uniform, it is potentially useful to
improve the robustness of the underlying descriptors in these areas.
Conclusions
We have presented new methods for face recognition under uncontrolled lighting based
on robust preprocessing and an extension of the Local Binary Pattern (LBP) local tex-
ture descriptor. There are three main contributions: (i) a simple, efficient image pre-
processing chain whose practical recognition performance is comparable to or better
than current (often much more complex) illumination normalization methods; (ii) a
rich descriptor for local texture called Local Ternary Patterns (LTP) that generalizes
LBP while fragmenting less under noise in uniform regions; and (iii) a distance trans-
form based similarity metric that captures the local structure and geometric variations
of LBP/LTP face images better than the simple grids of histograms that are currently
used. The combination of these enhancements provides very promising performance on
three well-known face datasets that contain widely varying lighting conditions.
Work in progress includes experiments on the much larger FRGC 2.0.4 dataset and
tests against subspace based recognition methods.
[attachment=62669]
Abstract
Recognition in uncontrolled situations is one of the most important
bottlenecks for practical face recognition systems. We address this by combining
the strengths of robust illumination normalization, local texture based face repre-
sentations and distance transform based matching metrics. Specifically, we make
three main contributions: (i) we present a simple and efficient preprocessing chain
that eliminates most of the effects of changing illumination while still preserving
the essential appearance details that are needed for recognition; (ii) we introduce
Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP)
local texture descriptor that is more discriminant and less sensitive to noise in
uniform regions; and (iii) we show that replacing local histogramming with a lo-
cal distance transform based similarity metric further improves the performance
of LBP/LTP based face recognition. The resulting method gives state-of-the-art
performance on three popular datasets chosen to test recognition under difficult
illumination conditions: Face Recognition Grand Challenge version 1 experiment
4, Extended Yale-B, and CMU PIE.
Introduction
One of the key challenges of face recognition is finding efficient and discriminative fa-
cial appearance descriptors that can counteract large variations in illumination, pose,
facial expression, ageing, partial occlusions and other changes [27]. There are two
main approaches: geometric feature-based descriptors and appearance-based descrip-
tors. Geometric descriptors can be hard to extract reliably under variations in facial
appearance, while appearance-based ones such as eigenfaces tend to blur out small de-
tails owing to residual spatial registration errors. Recently, representations based on
local pooling of local appearance descriptors have drawn increasing attention because
they can capture small appearance details in the descriptors while remaining resistant
to registration errors owing to local pooling. Another motivation is the observation that
human visual perception is well-adapted to extracting and pooling local structural in-
formation (‘micro-patterns’) from images [2]. Methods in this category include Gabor
wavelets [16], local autocorrelation filters [11], and Local Binary Patterns [1].
Related Work
As emphasized by the recent FRVT and FRGC trials [19], illumination variations are
one of the most important bottlenecks for practical face recognition systems. Gener-
ally, one can cope with this in two ways. The first uses training examples to learn a
global model of the possible illumination variations, for example a linear subspace or
manifold model, which then generalizes to the variations seen in new images [5,3]. The
disadvantage is that many training images are required.
The second approach seeks conventional image processing transformations that re-
duce the image to a more “canonical” form in which the variations are suppressed. This
has the merit of easy application to real images and the lack of a need for comprehensive
training data. Given that complete illumination invariants do not exist [7], one must con-
tent oneself with finding representations that are resistant to the most common classes
of natural illumination variations. Most methods exploit the fact that these are typically
characterized by relatively low spatial frequencies. For example, the Multiscale Retinex
(MSR) method of Jobson et al. [13] normalizes the illumination by dividing the image
by a smoothed version of itself.
Local Ternary Patterns (LTP)
LBP’s are resistant to lighting effects in the sense that they are invariant to monotonic
gray-level transformations, and they have been shown to have high discriminative power
for texture classification [17]. However because they threshold at exactly the value of
the central pixel ic they tend to be sensitive to noise, especially in near-uniform image
regions. Given that many facial regions are relatively uniform, it is potentially useful to
improve the robustness of the underlying descriptors in these areas.
Conclusions
We have presented new methods for face recognition under uncontrolled lighting based
on robust preprocessing and an extension of the Local Binary Pattern (LBP) local tex-
ture descriptor. There are three main contributions: (i) a simple, efficient image pre-
processing chain whose practical recognition performance is comparable to or better
than current (often much more complex) illumination normalization methods; (ii) a
rich descriptor for local texture called Local Ternary Patterns (LTP) that generalizes
LBP while fragmenting less under noise in uniform regions; and (iii) a distance trans-
form based similarity metric that captures the local structure and geometric variations
of LBP/LTP face images better than the simple grids of histograms that are currently
used. The combination of these enhancements provides very promising performance on
three well-known face datasets that contain widely varying lighting conditions.
Work in progress includes experiments on the much larger FRGC 2.0.4 dataset and
tests against subspace based recognition methods.