Abstract
During the past few years, there is an increasing demand for smart devices in
consumer electronics. These smart devices should be capable of consciously
sensing their surroundings and adapting their services according to their environments.
Face recognition provides a natural visual interface for such applications
and can be embedded into corresponding devices to facilitate context
awareness. The continuous growth of computing power brings face recognition
within the reach of consumer devices and embedded applications.
Compared to traditional face recognition in professional applications, face
recognition in embedded/consumer applications is characterized by a large
variability of operating environments and limited computation power and image
quality. We aim at designing a face-recognition system which has a performance
that is competitive to a professional system but has a significantly
higher efficiency in terms of computation.
In this thesis, we aim at employing multiple algorithms that coordinate
with each other for enhanced face-recognition performance while managing
the overall complexity. More specifically, we propose new techniques for three
major processing stages in face recognition, namely, face detection, facial feature
extraction and face identification. At each stage, our major contribution
is the design of a number of novel algorithms that are further combined into
a cascaded structure. In this cascaded framework, we focus mainly on the
following two aspects: (1) design of individual algorithms to meet system
requirements, and (2) optimization of algorithm ordering and interfacing to
improve the overall system performance.
For face detection, we have proposed two pruning detection cascades,
where we use fast detectors to quickly discard large non-face background areas
and more accurate detectors at succeeding stages to refine the detection
results. In this way, both the high detection accuracy and the processing efficiency
can be achieved simultaneously. The first cascade is based on various
feature detectors, namely, a color-based detector, a feature-geometry-based
detector and a neural-network-based detector. The second cascade is based
on a set of neural-network ensembles. First, for improved detection accuracy,
we propose a novel training technique to form a coordinated ensemble of neu
ral networks. Second, for improved detection efficiency, we build a cascade of
neural-network ensembles with scalable structures. The approach achieves one
of the highest detection accuracies in literature with a significantly reduced
computation cost. The proposed structure is also suitable to be implemented
in parallelized hardware architectures.
For facial feature extraction, we have first developed an improved algorithm
of the Active Shape Model (ASM), which extends ASM by using Haarbased
local feature modeling. The enhanced modeling enriches the representation
power of ASM and leads to doubled convergence capability and a 17% improvement
in accuracy. Afterwards, we have developed a cascaded extraction
framework for a set of model-based extraction algorithms. We have defined a
set of principles to guide the construction of such a framework, which examines
the performance relations between adjacent algorithms. As an implementation,
we propose a three-algorithm cascade for facial feature extraction, which
consists of a sparse graph model, a component-based texture model and a
component-based appearance model. These algorithms capture different characteristics
of facial features, giving an increasing extraction accuracy coupled
with a decreasing convergence. By tuning the performance of each algorithm
based on the output statistics of its preceding algorithm, a feature model can
be progressively ‘pulled’ to the correct position. The experiments show that
our approach is not sensitive to large model deviations and achieves a high
extraction accuracy (24% gain compared to ASM).
For face identification, we have explored a selective cascade for improved
identification performance. The selection cascade gradually reduces the candidate
size and derives a customized classification function for each candidate
set. We have applied Linear Discriminant Analysis in this framework and illustrated
the effectiveness of the approach (23% reduction of identification error).
Furthermore, we have investigated a new adaptive feature selection as a more
efficient implementation of the cascaded identification. This approach selects
a set of the most discriminating features for each person based on the so-called
class-specific maximum marginal diversity. According to the selected features,
an efficient matching function is defined. Our cascaded algorithm effectively
improves the identification of single algorithms and outperforms several wellknown
face identification algorithms (e.g. by a reduction of identification error
by 18%).
We have successfully applied selections of our developed face-recognition
techniques in several applications, such as smart user identification in a connected
home environment, face recognition for secure biometric identification
and face recognition for video surveillance and database retrieval. In several
extensive tests, the system has demonstrated competitive performance with
respect to accuracy, efficiency and robustness.
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 3 Oct 2006 |
Place of Publication | Eindhoven |
Publisher | |
Print ISBNs | 978-90-386-1863-0 |
DOIs | |
Publication status | Published - 2006 |