A Correlation Based Stereo Vision System for Face Recognition Applications April 2004 Daniel Bardsley (djb01u) [email protected]Page 1 of 56 A Correlation Based Stereo Vision System For Face Recognition Applications Daniel Bardsley [email protected]Supervised by: Bai Li [email protected]2004 University of Nottingham
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A Correlation Based Stereo Vision System for Face Recognition Applications April 2004 Daniel Bardsley (djb01u) [email protected]
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A Correlation Based Stereo Vision System For Face Recognition Applications
Figure 15: Simplified UML diagram of VisionLib, the library containing all the computer vision
related code within the project.
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11.3 Data Structures and Algorithms The major data structures defined within VisionLib are as follows:
• CvCalibFilter: This is the calibration object. VisionLib utilises this DirectX
DirectShow filter provided by OpenCV to obtain stereo rig calibration data for use
throughout the library.
• ImageData: This class holds all the image data required by the reconstruction.
• MatchingPoint: All of the data regarding point correspondence matches is stored in
this object. This includes x and y co-ordinates on both the left and the right images,
whether the point is deemed valid and the calculated strength of the match. This
object also contains methods for storing the calculated 3D position of the matched
point.
• Reconstruct: This object contains data structures and methods for surface
generation, including a linked-list of joined 3D points specifying the triangular surface
mesh.
The majority of the remaining classes interact with these four data structures to progress
through the different stages of the reconstruction. It is also the data in these three structures
that the views in the main application interface are designed to interact with and display via
the application document object.
In order to support the fast interchange and comparison of algorithms the VisionLib library
exhibits a high degree of polymorphism. The set of objects relating to stereo reconstruction
are derived from the Stereo class. This defines a set of virtual methods which derived objects
must implement. This allows for a consistent interface to each of the different algorithms.
The three sets of algorithms that are derived from the stereo class are Input, Match and
Constraint. Input contains subclasses for handling input point selection, ie. which initial points
we will attempt to correlate. Match contains subclasses for finding corresponding image
points and Constraint handles constraining these matches. Each of these three objects are
further subclasses to provide the actual functionality. For example the Match class is
subclassed to provide implementations of the actual matching algorithms. Currently
implemented here is the SSD and ZMNCC algorithms investigated in the Correlation section
of this report.
Some of the more important aspects of some specific algorithm implementations are
described below:
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Input Objects:
• CvGoodTrackingFeatures: This algorithm is based on a method within the
OpenCV library. It takes an image which has had a binary threshold filter applied
to it and uses a modification of the Harris detector to find feature points with a
good chance of being matched. The binary threshold filter and point detector can
be applied under at a number of different thresholds simultaneously to provide a
point set which is evenly distributed over the target object.
• PatternGrid: Feature points are chosen by overlaying the input image with
regularly spaced input points which form a rectangular grid. This allows every
pixel in the input image to be selected, either for reconstruction purposes or to
attempt to create a dense disparity map. This is only useful when we need
regular input point spacing, since it makes no guarantees that the points will
make good matches.
• Manual: Input points can be manually entered via a text file in the form of 2D co-
ordinates.
Match Objects:
• SSD and ZMNCC: Both these correlation algorithms behave as described in the
appropriate section of this report except they have both been modified for
improved performance on colour images. The algorithms can optionally take into
consideration information from all three colour channels to help differentiate
between closely contested best matches.
• Manual: Matching points can be entered via a text file containing a list of 2D co-
ordinates.
Constraint Objects:
• Similarity: During the correlation phase a match strength is calculated for each
point candidate based on the similarity of the point and the surrounding area.
When the similarity constraint is applied all points with a match strength below a
given value are marked as invalid.
• Uniqueness: Each point in a dataset is tested for uniqueness with all other co-
ordinates in the dataset.
• Statistical: Certain assumptions can be made about the reconstructed data.
Assuming a normal distribution of the 3D points we can eliminate points that fall
outside a given standard distribution and hence remove some points that may
have been matched incorrectly.
A number of other important classes exist in the library which are not specific to stereo
matching and hence are not derived from the Stereo object. These are Tools, PrePro and
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Reconstruct. Tools contains miscellaneous tools which do not fall into other categories but
are useful vision algorithms none the less. For example in some cases it may be useful to
find a face within a given image and hence the Tools class contains methods for performing
such tasks. The face finding algorithm is based on Haar feature cascade [54] and is a direct
implementation of functionality provided by the OpenCV library. The PrePro class contains
functionality regarding 2D image manipulation which may be useful in stages prior to
matching. Functionality such as histogram matching is provided which has potential uses in
invariant illumination matching across input images. Finally the Reconstruct object forms the
basis for a set of reconstruction algorithms. This set of objects take 3D point cloud data as
input and return a predicted surface. Bourke’s modified version of Delaunay triangulation is
implemented here.
11.4 Implementation Results The current implementation of the face scanner vision system meets most of the
requirements specified as goals at the beginning of the implementation. We have indeed
implemented a system that is “capable of reconstructing the visible surface of an object given
left and right images of a stereo pair and calibration data.” We have not implemented a
general vision system, instead focussing on the reconstruction of face objects. Furthermore
the implementation is capable of obtaining accurate calibration data from a sequence of
images containing an appropriate calibration pattern. The application is also successful in
separating vision code from GUI code to ensure maximum re-usability of promising vision
algorithms. The structure of the vision library code also satisfies the goal that the system
“allows future expansion and fast integration of improved algorithms.” This is achieved
through the implementation of a polymorphic class structure within the library.
This enables every component of a vision process to interact with every other component
without regard for the specific algorithms in use. Finally, the separation of the vision code
from the GUI code has the additional benefit of slightly increasing the ease with which the
vision code could be ported to another platform since only the GUI code is heavily platform
dependant.
With regards to the goal that the application should provide output suitable for the basis of a
pose-invarient face recognition system it is unclear if the current application would meet these
requirements. Since we currently have no frontal face recognition system available for testing
purposes the requirements for “good” models for recognition are unclear and hence our
application can not be tested for suitability. It is likely however that a number of additional
features would be required. For example when a face is viewed in a none frontal pose and
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then rotated to a frontal pose prior to recognition it is possible non-visible surfaces are now
visible.
Figure 16: FaceScanner application screenshot
These surfaces must be estimated to allow proper recognition to take place. Symmetric
properties of the face suggest that we could estimate the missing surfaces from the data
already available. At this stage algorithms aimed at solving this set of missing-data surface
reconstruction problems are a target for future work. The current implementation of the
application does however show some promise in this department since the calibration,
reconstruction and application framework currently in place have proved to be both correct
and accurate. The point correlation algorithms, whilst correct, are relatively basic and more
advanced correlation algorithms need to be implemented. The application is, however,
relatively successful in meeting the demands set by the implementation goals. Figure 16
shows a screenshot of the application running with a reconstruction in progress.
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12 Software Libraries
A number of libraries and API’s were found to be useful during the development of the stereo
vision system. The libraries which were used are listed below.
Intel’s Image Processing Library (v2.5) This library provides low level functionality for processing bitmaps, JPEGs and other image
formats. Whilst the stereo vision system does not implement much IPL functionality directly
libraries such as OpenCV rely heavily on functionality provided by this library.
Intel’s OpenCV (v3.1.Beta) This open source computer vision library contains a mass of functions for many computer
vision related tasks. These include functions from camera calibration and disparity estimation
to the computation of optical flow. This library has since been superseded by the Intel
Performance Primitives library, however, much functionality is reportedly identical to that of
OpenCV. Much of the OpenCV functionality is based on lower level functions provided by the
Intel Performance Primitives Library.
Intel’s Math Kernel Library (v6.1.009) Our application make use of the linear algebra sections of this maths library. The linear
algebra / least squares technique is used in order to project correlated image points back into
3D space. The routines in linear algebra routines in MKL are based on those implemented in
LAPACK.
Microsoft DirectX SDK (v9.0) Several of the image capture and camera control routines are based on the DirectX SDK.
The calibration process is also implemented as a DirectX filter.
Microsoft Foundation Classes The windows interface is programmed to take full advantage of available MFC resources with
the current implementation supporting the Multiple Document Interface Document/View
architecture to allow simultaneous, complex views of the large datasets which we have to
deal with throughout the course of a reconstruction.
OpenGL All 3D views of our data are rendered using the OpenGL library. This selection was made
rather than alternatives, such as Direct3D, because of its programming simplicity and its wide
support both from application programmers and hardware designers. Furthermore OpenGL is
much more platform independent that the Direct3D library.
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A number of applications were utilized in the development of the stereo vision system.
Microsoft Visual Studio 6.0 C++ application development was carried out exclusively in this industry standard
development tool. Selected primarily for its MFC support and visual application development.
C++ was selected as the development language due to its speed, versatility and support
which supercedes that achievable through interpreted languages such as Java.
Mathworks Matlab 12 The more mathematically based algorithms within the system were tested for correctness and
underwent fast track development using this matrix evaluation application.
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13 Results The system was tested with a variety of data under a number of conditions. Most of the
system tests we implemented using synthesized images. The reason for this is that it is
easier to eliminate unwanted input features such as noise or illumination variations.
Furthermore through the use of synthesized images a correct version of the model we are
trying to reconstruct already exists and hence we have data with which we can compare our
results. Much of the output from the system has been included in earlier sections in order to
demonstrate the correctness of various sub-systems. The project meets the majority of the
goals we set out to achieve. Significant research and development has been carried out into
stereo camera calibration, the correspondence problem, 3D projective reconstruction and
surface estimation. Further to this an implementation of a vision system aimed at tackling and
solving the problems brought about by the reconstruction process has been developed with
the results obtained from the program demonstrating an acceptable level of accuracy.
Practical evidence suggests that the calibration process performs correctly. The calibration
section of this report details some results from a sequence of synthesized images from a
known rig calibration and demonstrates that the results obtained represent the properties of
the actual rig correctly. Testing of the calibration procedures on both real image sequences
and live video have also produced accurate results. Furthermore testing of the system under
a number of varying camera rigs demonstrated that this implementation of the calibration
routines work under the majority of general stereo rig calibrations.
Perhaps the most error-prone area of the reconstruction process at present is the
correspondence matching phase. The correspondence problem is widely considered the
most difficult area of reconstruction and this is demonstrated by our implementation. As
demonstrated in the correspondence section of this report both the SSD and ZMNCC
algorithms perform well and produce good disparity maps however these simple pixel-wise
intensity based algorithms prove to be too light weight to perform well under general
reconstruction conditions. Simple intensity based algorithms are unlikely to yield a quality
solution to this particular correspondence problem. The major reasoning behind this is simply
that correct point matches are often too similar to incorrect candidate matches for intensity
based methods to correctly differentiate between them. The situation is complicated further
by noise during the image capture process of varying light levels between a stereo pair.
These image features cause major errors in the correspondence phase which propagate to
other stages. The addition of numerous matching constraints improves matters at the cost of
reducing the degree of automation present in the system, however, they do not provide a
perfect solution and erroneous points are still matched. This system is unlikely to be
improved much further through the addition of more constraints and as such the development
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of more advanced matching algorithms using none intensity based methods is going to be a
primary goal of any additional work. In order for the currently implemented correlation
techniques to be effective we need to be matching only a small number of highly salient input
points. This would increase the likelihood of obtaining an accurate result set at the expense
of having a smaller number of points to work with, and hence a less accurate resultant
surface.
Once a set of matches has been found and constrained we can commence with the actual
reconstruction. The calculations behind the 3D projection have been researched in the past
and with the existence of the appropriate projection equations reconstruction to a 3D point
cloud is relatively trivial. The reconstruction stage demonstrates a cube and a face model
reconstruction which serve to demonstrate the accuracy of the technique. Surface
reconstruction from the point cloud provides adequate results using Burke’s Delauney
implementation. Some consideration should be taken into account by the meshing algorithm
of potential errors in previous stages, however, at this time this is not taking place and hence
the surface reconstruction stage does nothing to “smooth over” the errors in the
correspondence stage. To this end a more sophisticated algorithm could be implemented
which attempts to create a smooth surface possibly using techniques such as Beizer curves.
An implementation of the marching cubes algorithm would also provide an interesting
comparison in terms of producing a surface with more desirable properties than that of the
mesh currently produced. Furthermore if the system were to be involved as a recognition
subsystem then it would be essential to consider algorithms for hidden surface reconstruction
to enable rendering of surfaces of the face no initial visible in the input images. As an
alternative to estimating the hidden surface it may well prove useful to implement a system
that utilizes a generic head model to aid reconstruction, this may also prove a viable solution
to increasing the effectiveness of the currently implemented intensity based correlation
algorithms, since a smaller number of points would have to be required due to the volume of
data already available to the system. The current system is already capable of selecting only
input points with a high chance of being matched (using the GoodTrackingFeatures input
algorithm) and as such the framework is already suitable for the addition of a generic head
model.
Figure 17 shows the results of a fully automated reconstruction after a number of thresholds
were set and calibration data acquired. The images used were again synthesized since at
this point it is difficult to obtain accurate reconstructions from actual imagery due to problems
described with the correspondence algorithms above. Also the images were created in such
a manner that the correspondence algorithms would find matches easier to make.
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Figure 17: Fully automatic reconstruction of a synthesized face from stereo images. White
dots on the 3d model show initial point match positions.
It should be obvious from the output produced that the correspondence algorithms struggled
even under these constrained conditions and still produced numerous incorrect matches,
many of these were eliminated with the application of constraints however the correlation
algorithms are simply not accurate enough to perform on real world data at this point.
Despite some correlation accuracy problems the system performs well throughout. It should
be noted however that problems with the correspondence stage of the reconstruction appear
to be due to properties of the matching algorithms involved rather than fundamental problems
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with the system. Furthermore we have been successful in creating an application with a
framework such that it is possible to implement new and more efficient algorithms easily and
integrate them with the system with major problems. This has the advantage that despite the
under performance of the current correspondence algorithms new algorithms which show
promise such as wavelet decomposition and matching can be implemented and integrated
into the system so their performance can be analyzed. Thus, despite some errors within
certain areas of the system these errors can be observed and algorithms that perform at a
lower error rate introduced into the system with ease.
The implementation of the FaceScanner application and the development of the VisionLib
library have led to the creation of a successful architecture for investigating various vision
algorithms and reconstruction techniques and thus has proved to be a useful application
implementation. Furthermore the interface with which the user can interact with the system is
of potential commercial quality allowing simple guidance of the reconstruction processes
fueled by intuitive data representation. The reuseable nature of each of the system
components allows future development within the current application framework to improve
current performance.
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14 Conclusions and Future Work The majority of the goals specified at the beginning of this report have been met. Successful
research has gone into each stage of the reconstruction process and the system is geared
towards working with a set of CCTV camera. At this stage autonomous reconstruction from
real imagery has not been achieved due to an partially inadequate solution to the
correspondence problem, however, the framework is in place and capable of supporting
future reconstructions in the presence of more powerful matching algorithms. The system
has however proved a number of techniques to be correct and well suited to the task of facial
reconstruction. Testing carried out on synthesized input yielded accurate results in areas of
the system where they were expected. A working implementation of a vision system capable
of stereo calibration and reconstruction has been developed and adheres to the design goals
specified in the implementation section.
With regards to the usefulness of the system output as input to a recognition subsystem the
results are inconclusive. Additional features would certainly have to be implemented and
point matching improved to ensure we could construct an accurate face model, however the
basis for such as system is in place. The addition of hidden surface reconstruction possibly
through the use a generic head model would be essential in ensuring we can recreate
recognizable face models. The development of more accurate point matching algorithms
should probably be the focus of future work since this is the area where currently the
application struggles to perform. This could be further aided by the use of different algorithms
in the feature point detection stage, despite the current algorithm performing reasonably well.
Finally improvements to surface estimation with the addition of mesh smoothing to eliminate
errors from earlier stages would probably provide a system which was very suitable for use in
a pose-invariant face recognition system despite the face that the system is not currently at
this stage.
The stereo reconstruction problem is one of constrained optimization. The existence of
numerous stages in the system leads to the propagation of estimation errors throughout. By
increasing accuracy in any way possible and constraining each part of the system to eliminate
most errors we have produced a system which to a degree is capable of face surface
reconstructions. Despite containing stages which, under some conditions, fail to perform the
system produces accurate results in general. The majority of initial design goals have been
satisfied and with the implementation of some additional algorithms the system could be
made to completely for-fill all aims and goals and find application in face recognition utilities.
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