540 research outputs found
A survey of face recognition techniques under occlusion
The limited capacity to recognize faces under occlusions is a long-standing
problem that presents a unique challenge for face recognition systems and even
for humans. The problem regarding occlusion is less covered by research when
compared to other challenges such as pose variation, different expressions,
etc. Nevertheless, occluded face recognition is imperative to exploit the full
potential of face recognition for real-world applications. In this paper, we
restrict the scope to occluded face recognition. First, we explore what the
occlusion problem is and what inherent difficulties can arise. As a part of
this review, we introduce face detection under occlusion, a preliminary step in
face recognition. Second, we present how existing face recognition methods cope
with the occlusion problem and classify them into three categories, which are
1) occlusion robust feature extraction approaches, 2) occlusion aware face
recognition approaches, and 3) occlusion recovery based face recognition
approaches. Furthermore, we analyze the motivations, innovations, pros and
cons, and the performance of representative approaches for comparison. Finally,
future challenges and method trends of occluded face recognition are thoroughly
discussed
A survey of face recognition techniques under occlusion
The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed
Photorealistic retrieval of occluded facial information using a performance-driven face model
Facial occlusions can cause both human observers and computer algorithms
to fail in a variety of important tasks such as facial action analysis and
expression classification. This is because the missing information is not
reconstructed accurately enough for the purpose of the task in hand. Most
current computer methods that are used to tackle this problem implement
complex three-dimensional polygonal face models that are generally timeconsuming
to produce and unsuitable for photorealistic reconstruction of
missing facial features and behaviour.
In this thesis, an image-based approach is adopted to solve the occlusion
problem. A dynamic computer model of the face is used to retrieve the
occluded facial information from the driver faces. The model consists of a
set of orthogonal basis actions obtained by application of principal
component analysis (PCA) on image changes and motion fields extracted
from a sequence of natural facial motion (Cowe 2003). Examples of
occlusion affected facial behaviour can then be projected onto the model to
compute coefficients of the basis actions and thus produce photorealistic
performance-driven animations.
Visual inspection shows that the PCA face model recovers aspects of
expressions in those areas occluded in the driver sequence, but the expression is generally muted. To further investigate this finding, a database
of test sequences affected by a considerable set of artificial and natural
occlusions is created. A number of suitable metrics is developed to measure
the accuracy of the reconstructions. Regions of the face that are most
important for performance-driven mimicry and that seem to carry the best
information about global facial configurations are revealed using Bubbles,
thus in effect identifying facial areas that are most sensitive to occlusions.
Recovery of occluded facial information is enhanced by applying an
appropriate scaling factor to the respective coefficients of the basis actions
obtained by PCA. This method improves the reconstruction of the facial
actions emanating from the occluded areas of the face. However, due to the
fact that PCA produces bases that encode composite, correlated actions,
such an enhancement also tends to affect actions in non-occluded areas of
the face. To avoid this, more localised controls for facial actions are
produced using independent component analysis (ICA). Simple projection
of the data onto an ICA model is not viable due to the non-orthogonality of
the extracted bases. Thus occlusion-affected mimicry is first generated using
the PCA model and then enhanced by accordingly manipulating the
independent components that are subsequently extracted from the mimicry.
This combination of methods yields significant improvements and results in
photorealistic reconstructions of occluded facial actions
Facial Expression Analysis under Partial Occlusion: A Survey
Automatic machine-based Facial Expression Analysis (FEA) has made substantial
progress in the past few decades driven by its importance for applications in
psychology, security, health, entertainment and human computer interaction. The
vast majority of completed FEA studies are based on non-occluded faces
collected in a controlled laboratory environment. Automatic expression
recognition tolerant to partial occlusion remains less understood, particularly
in real-world scenarios. In recent years, efforts investigating techniques to
handle partial occlusion for FEA have seen an increase. The context is right
for a comprehensive perspective of these developments and the state of the art
from this perspective. This survey provides such a comprehensive review of
recent advances in dataset creation, algorithm development, and investigations
of the effects of occlusion critical for robust performance in FEA systems. It
outlines existing challenges in overcoming partial occlusion and discusses
possible opportunities in advancing the technology. To the best of our
knowledge, it is the first FEA survey dedicated to occlusion and aimed at
promoting better informed and benchmarked future work.Comment: Authors pre-print of the article accepted for publication in ACM
Computing Surveys (accepted on 02-Nov-2017
State of the Art in Face Recognition
Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state
A SURVEY : FACE RECOGNITION UNDER OCCLUSION CONDITION
IndiaFace recognition is a pattern recognition task performed specifically on faces. It can be described as classifying a face either âknownâ or unknown, after comparing with stored known individuals. It is also desirable to have a system that has the ability of learning to recognize unknown faces. Computational models of face recognition must address several difficulat problems. This difficulty arises from the face that faces must be represented in a way that best utilizes the available face information to distinguish a particular face fro all other faces. Faces pose a particularly difficult in this respect because all faces are similar to one another in that they contain the same set of features such as eyes, nose mouth arranges in roughly the same manner. There are several types of face recogniton systems discussed in the literature. Geometry and templates, Template matching, Dynamic Deformable Templates, Independent Component Analysis, Wavelets, Gabor Fisher Classifiers, Hidden Markow Models and Neural Network. This survey will be very useful for any future scholars to work in this domain using all the collected references
Novel Deep Learning Techniques For Computer Vision and Structure Health Monitoring
This thesis proposes novel techniques in building a generic framework for both the regression and classification tasks in vastly different applications domains such as computer vision and civil engineering. Many frameworks have been proposed and combined into a complex deep network design to provide a complete solution to a wide variety of problems. The experiment results demonstrate significant improvements of all the proposed techniques towards accuracy and efficiency
Robust subspace learning for static and dynamic affect and behaviour modelling
Machine analysis of human affect and behavior in naturalistic contexts has witnessed a growing attention in the last decade from various disciplines ranging from social and cognitive sciences to machine learning and computer vision. Endowing machines with the ability to seamlessly detect, analyze, model, predict as well as simulate and synthesize manifestations of internal emotional and behavioral states in real-world data is deemed essential for the deployment of next-generation, emotionally- and socially-competent human-centered interfaces. In this thesis, we are primarily motivated by the problem of modeling, recognizing and predicting spontaneous expressions of non-verbal human affect and behavior manifested through either low-level facial attributes in static images or high-level semantic events in image sequences. Both visual data and annotations of naturalistic affect and behavior naturally contain noisy measurements of unbounded magnitude at random locations, commonly referred to as âoutliersâ. We present here machine learning methods that are robust to such gross, sparse noise. First, we deal with static analysis of face images, viewing the latter as a superposition of mutually-incoherent, low-complexity components corresponding to facial attributes, such as facial identity, expressions and activation of atomic facial muscle actions. We develop a robust, discriminant dictionary learning framework to extract these components from grossly corrupted training data and combine it with sparse representation to recognize the associated attributes. We demonstrate that our framework can jointly address interrelated classification tasks such as face and facial expression recognition. Inspired by the well-documented importance of the temporal aspect in perceiving affect and behavior, we direct the bulk of our research efforts into continuous-time modeling of dimensional affect and social behavior. Having identified a gap in the literature which is the lack of data containing annotations of social attitudes in continuous time and scale, we first curate a new audio-visual database of multi-party conversations from political debates annotated frame-by-frame in terms of real-valued conflict intensity and use it to conduct the first study on continuous-time conflict intensity estimation. Our experimental findings corroborate previous evidence indicating the inability of existing classifiers in capturing the hidden temporal structures of affective and behavioral displays. We present here a novel dynamic behavior analysis framework which models temporal dynamics in an explicit way, based on the natural assumption that continuous- time annotations of smoothly-varying affect or behavior can be viewed as outputs of a low-complexity linear dynamical system when behavioral cues (features) act as system inputs. A novel robust structured rank minimization framework is proposed to estimate the system parameters in the presence of gross corruptions and partially missing data. Experiments on prediction of dimensional conflict and affect as well as multi-object tracking from detection validate the effectiveness of our predictive framework and demonstrate that for the first time that complex human behavior and affect can be learned and predicted based on small training sets of person(s)-specific observations.Open Acces
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