10,076 research outputs found
Joint optimization of manifold learning and sparse representations for face and gesture analysis
Face and gesture understanding algorithms are powerful enablers in intelligent vision systems for surveillance, security, entertainment, and smart spaces. In the future, complex networks of sensors and cameras may disperse directions to lost tourists, perform directory lookups in the office lobby, or contact the proper authorities in case of an emergency. To be effective, these systems will need to embrace human subtleties while interacting with people in their natural conditions. Computer vision and machine learning techniques have recently become adept at solving face and gesture tasks using posed datasets in controlled conditions. However, spontaneous human behavior under unconstrained conditions, or in the wild, is more complex and is subject to considerable variability from one person to the next. Uncontrolled conditions such as lighting, resolution, noise, occlusions, pose, and temporal variations complicate the matter further. This thesis advances the field of face and gesture analysis by introducing a new machine learning framework based upon dimensionality reduction and sparse representations that is shown to be robust in posed as well as natural conditions. Dimensionality reduction methods take complex objects, such as facial images, and attempt to learn lower dimensional representations embedded in the higher dimensional data. These alternate feature spaces are computationally more efficient and often more discriminative. The performance of various dimensionality reduction methods on geometric and appearance based facial attributes are studied leading to robust facial pose and expression recognition models. The parsimonious nature of sparse representations (SR) has successfully been exploited for the development of highly accurate classifiers for various applications. Despite the successes of SR techniques, large dictionaries and high dimensional data can make these classifiers computationally demanding. Further, sparse classifiers are subject to the adverse effects of a phenomenon known as coefficient contamination, where for example variations in pose may affect identity and expression recognition. This thesis analyzes the interaction between dimensionality reduction and sparse representations to present a unified sparse representation classification framework that addresses both issues of computational complexity and coefficient contamination. Semi-supervised dimensionality reduction is shown to mitigate the coefficient contamination problems associated with SR classifiers. The combination of semi-supervised dimensionality reduction with SR systems forms the cornerstone for a new face and gesture framework called Manifold based Sparse Representations (MSR). MSR is shown to deliver state-of-the-art facial understanding capabilities. To demonstrate the applicability of MSR to new domains, MSR is expanded to include temporal dynamics. The joint optimization of dimensionality reduction and SRs for classification purposes is a relatively new field. The combination of both concepts into a single objective function produce a relation that is neither convex, nor directly solvable. This thesis studies this problem to introduce a new jointly optimized framework. This framework, termed LGE-KSVD, utilizes variants of Linear extension of Graph Embedding (LGE) along with modified K-SVD dictionary learning to jointly learn the dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and sparsity-based classifier. By injecting LGE concepts directly into the K-SVD learning procedure, this research removes the support constraints K-SVD imparts on dictionary element discovery. Results are shown for facial recognition, facial expression recognition, human activity analysis, and with the addition of a concept called active difference signatures, delivers robust gesture recognition from Kinect or similar depth cameras
Dynamics of trimming the content of face representations for categorization in the brain
To understand visual cognition, it is imperative to determine when, how and with what information the human brain categorizes the visual input. Visual categorization consistently involves at least an early and a late stage: the occipito-temporal N170 event related potential related to stimulus encoding and the parietal P300 involved in perceptual decisions. Here we sought to understand how the brain globally transforms its representations of face categories from their early encoding to the later decision stage over the 400 ms time window encompassing the N170 and P300 brain events. We applied classification image techniques to the behavioral and electroencephalographic data of three observers who categorized seven facial expressions of emotion and report two main findings: (1) Over the 400 ms time course, processing of facial features initially spreads bilaterally across the left and right occipito-temporal regions to dynamically converge onto the centro-parietal region; (2) Concurrently, information processing gradually shifts from encoding common face features across all spatial scales (e.g. the eyes) to representing only the finer scales of the diagnostic features that are richer in useful information for behavior (e.g. the wide opened eyes in 'fear'; the detailed mouth in 'happy'). Our findings suggest that the brain refines its diagnostic representations of visual categories over the first 400 ms of processing by trimming a thorough encoding of features over the N170, to leave only the detailed information important for perceptual decisions over the P300
Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application
Spontaneous subtle emotions are expressed through micro-expressions, which
are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great
challenge for visual recognition. The abrupt but significant dynamics for the
recognition task are temporally sparse while the rest, irrelevant dynamics, are
temporally redundant. In this work, we analyze and enforce sparsity constrains
to learn significant temporal and spectral structures while eliminate
irrelevant facial dynamics of micro-expressions, which would ease the challenge
in the visual recognition of spontaneous subtle emotions. The hypothesis is
confirmed through experimental results of automatic spontaneous subtle emotion
recognition with several sparsity levels on CASME II and SMIC, the only two
publicly available spontaneous subtle emotion databases. The overall
performances of the automatic subtle emotion recognition are boosted when only
significant dynamics are preserved from the original sequences.Comment: IEEE Transaction of Affective Computing (2016
Island Loss for Learning Discriminative Features in Facial Expression Recognition
Over the past few years, Convolutional Neural Networks (CNNs) have shown
promise on facial expression recognition. However, the performance degrades
dramatically under real-world settings due to variations introduced by subtle
facial appearance changes, head pose variations, illumination changes, and
occlusions.
In this paper, a novel island loss is proposed to enhance the discriminative
power of the deeply learned features. Specifically, the IL is designed to
reduce the intra-class variations while enlarging the inter-class differences
simultaneously. Experimental results on four benchmark expression databases
have demonstrated that the CNN with the proposed island loss (IL-CNN)
outperforms the baseline CNN models with either traditional softmax loss or the
center loss and achieves comparable or better performance compared with the
state-of-the-art methods for facial expression recognition.Comment: 8 pages, 3 figure
Objective Classes for Micro-Facial Expression Recognition
Micro-expressions are brief spontaneous facial expressions that appear on a
face when a person conceals an emotion, making them different to normal facial
expressions in subtlety and duration. Currently, emotion classes within the
CASME II dataset are based on Action Units and self-reports, creating conflicts
during machine learning training. We will show that classifying expressions
using Action Units, instead of predicted emotion, removes the potential bias of
human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D
feature descriptors. The experiments are evaluated on two benchmark FACS coded
datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when
classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the
result of the state-of-the-art 5-class emotional-based classification in CASME
II. Results indicate that classification based on Action Units provides an
objective method to improve micro-expression recognition.Comment: 11 pages, 4 figures and 5 tables. This paper will be submitted for
journal revie
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