text: Self-similarity was recently introduced as a measure of inter-class congruence for classification of actions. Herein, we investigate the dual problem of intra-class dissimilarity for classification of action styles. We introduce self-dissimilarity matrices that discriminate between same actions performed by different subjects regardless of viewing direction and camera parameters. We investigate two frameworks using these invariant style dissimilarity measures based on Principal Component Analysis (PCA) and Fisher Discriminant Analysis (FDA). Extensive experiments performed on IXMAS dataset indicate remarkably good discriminant characteristics for the proposed invariant measures for gender recognition from video data.
  @cite_127: We seek to analyze and manipulate two factors, which we call style and content, underlying a set of observations. We fit training data with bilinear models which explicitly represent the two-factor structure. These models can adapt easily during testing to new styles or content, allowing us to solve three general tasks: extrapolation of a new style to unobserved content; classification of content observed in a new style; and translation of new content observed in a new style. For classification, we embed bilinear models in a probabilistic framework, Separable Mixture Models (SMMs), which generalizes earlier work on factorial mixture models [7, 3]. Significant performance improvement on a benchmark speech dataset shows the benefits of our approach.
  @cite_58: 
  @cite_6: 
  @cite_66: Human movements include limb gestures and postural attitude. Although many computer animation researchers have studied these classes of movements, procedurally generated movements still lack naturalness. We argue that looking only at the psychological notion of gesture is insufficient to capture movement qualities needed by animated charactes. We advocate that the domain of movement observation science, specifically Laban Movement Analysis (LMA) and its Effort and Shape components, provides us with valuable parameters for the form and execution of qualitative aspects of movements. Inspired by some tenets shared among LMA proponents, we also point out that Effort and Shape phrasing across movements and the engagement of the whole body are essential aspects to be considered in the search for naturalness in procedurally generated gestures. Finally, we present EMOTE (Expressive MOTion Engine), a 3D character animation system that applies Effort and Shape qualities to independently defined underlying movements and thereby generates more natural synthetic gestures.
  @cite_24: A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms.
  @cite_57: 
  @cite_94: A method for the representation, recognition, and interpretation of parameterized gesture is presented. By parameterized gesture we mean gestures that exhibit a systematic spatial variation; one example is a point gesture where the relevant parameter is the two-dimensional direction. Our approach is to extend the standard hidden Markov model method of gesture recognition by including a global parametric variation in the output probabilities of the HMM states. Using a linear model of dependence, we formulate an expectation-maximization (EM) method for training the parametric HMM. During testing, a similar EM algorithm simultaneously maximizes the output likelihood of the PHMM for the given sequence and estimates the quantifying parameters. Using visually derived and directly measured three-dimensional hand position measurements as input, we present results that demonstrate the recognition superiority of the PHMM over standard HMM techniques, as well as greater robustness in parameter estimation with respect to noise in the input features. Finally, we extend the PHMM to handle arbitrary smooth (nonlinear) dependencies. The nonlinear formulation requires the use of a generalized expectation-maximization (GEM) algorithm for both training and the simultaneous recognition of the gesture and estimation of the value of the parameter. We present results on a pointing gesture, where the nonlinear approach permits the natural spherical coordinate parameterization of pointing direction.
  @cite_144: 
  @cite_49: Human motion is the composite consequence of multiple elements, including the action performed and a motion signature that captures the distinctive pattern of movement of a particular individual. We develop an algorithm that is capable of extracting these motion elements and recombining them in novel ways. The algorithm analyzes motion data spanning multiple subjects performing different actions. The analysis yields a generative motion model that can synthesize new motions in the distinctive styles of these individuals. Our algorithms can also recognize people and actions from new motions by comparing motion signatures and action parameters.
  @cite_63: Natural images are the composite consequence of multiple factors related to scene structure, illumination, and imaging. Multilinear algebra, the algebra of higher-order tensors, offers a potent mathematical framework for analyzing the multifactor structure of image ensembles and for addressing the difficult problem of disentangling the constituent factors or modes. Our multilinear modeling technique employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the N-mode SVD. As a concrete example, we consider the multilinear analysis of ensembles of facial images that combine several modes, including different facial geometries (people), expressions, head poses, and lighting conditions. Our resulting "TensorFaces" representation has several advantages over conventional eigenfaces. More generally, multilinear analysis shows promise as a unifying framework for a variety of computer vision problems.
  @cite_80: Given a single motion-capture sequence of a person performing a dynamic activity at a particular intensity (or effort), our goal is to automatically warp that movement into a natural-looking exaggerated version of that action. Consider warping a movement of a person lifting a lightweight box to make the movement appear as if the box were actually very heavy. We describe an efficient data-driven approach applicable to animation re-use that learns the underlying regularity in an action to select the most "expressive" features to exaggerate. Other "style-based" approaches are presented in [Gleicher 1998; Brand and Hertzmann 2000; Vasilescu 2001].
  @cite_134: This paper reports the first phase of a research program on visual perception of motion patterns characteristic of living organisms in locomotion. Such motion patterns in animals and men are termed here as biological motion. They are characterized by a far higher degree of complexity than the patterns of simple mechanical motions usually studied in our laboratories. In everyday perceptions, the visual information from biological motion and from the corresponding figurative contour patterns (the shape of the body) are intermingled. A method for studying information from the motion pattern per se without interference with the form aspect was devised. In short, the motion of the living body was represented by a few bright spots describing the motions of the main joints. It is found that 10–12 such elements in adequate motion combinations in proximal stimulus evoke a compelling impression of human walking, running, dancing, etc. The kinetic-geometric model for visual vector analysis originally developed in the study of perception of motion combinations of the mechanical type was applied to these biological motion patterns. The validity of this model in the present context was experimentally tested and the results turned out to be highly positive.
  @cite_68: We present a three-mode expressive-feature model for recognizing gender (female, male) from point-light displays of walking people. Prototype female and male walkers are initially decomposed into a subspace of their three-mode components (posture, time, and gender). We then apply a weight factor to each point-light trajectory in the basis representation to enable adaptive, context-based gender estimations. The weight values are automatically learned from labeled training data. We present experiments using physical (actual) and perceived (from perceptual experiments) gender labels to train and test the system. Results with 40 walkers demonstrate greater than 90 recognition for both physically and perceptually-labeled training examples. The approach has a greater flexibility over standard squared-error gender estimation to successfully adapt to different matching contexts.
  @cite_12: We present an approach for recognizing human walking movements using low-level motion regularities and constraints. Biomechanical features for classification are automatically extracted from video sequences of walkers. A multiplicative classification rule using statistical distances is then used to determine whether an unknown motion is consistent with normal walking patterns. Recognition results are shown distinguishing walking examples across multiple speeds from other nonwalking locomotions.
  @cite_105: We present an approach for visual discrimination of children from adults in video using characteristic regularities present in their locomotion patterns. The framework employs computer vision to analyze correlated, scale invariant locomotion properties for classifying different styles of walking. Male and female subjects for the experiments include six children (3-5 yrs) and nine adults (30-52 yrs). For the analysis, we coordinate a minimalist point-representation of the human body with a space-time analysis of head and ankle trajectories to characterize the modality. Together the properties of relative stride length and stride frequency are shown to clearly differentiate children from adult walkers. The highly correlated log-linear relationships for the stride properties are exploited to reduce the categorization problem to a linear discrimination task. Using a trained two-class linear perceptron, we were able to achieve a correct classification rate of 93-95 on our dataset. Our approach emphasizing the natural modal behavior in human motion offers a useful and general methodology as the basis for designing efficient motion recognition systems using limited visual features.
summary: It has been proven that humans can recognize actions from limited types of input such as point lights and low quality video @cite_24 @cite_134 . Even with such limited information, we are capable to differentiate stylistic action differences, such as the gender @cite_57 @cite_144 and age @cite_105 of a walking person. There has been several recent work on the study of action style variation in computer vision. Wilson and Bobick @cite_94 use a Parameterized-HMM to model spatial pointing gestures by adding a global variation paramters in the output probabilities of the HMM states. In @cite_127 use a bilinear model to separate perceptual content and style parameters. Davis @cite_105 proposed an approach to determine age of people based on variations in relative stride length and stride frequency over various walking speeds. In @cite_12 Davis and Taylor use regularities in walking to classify typical from atypical gaits. @cite_68 presented a three-mode (body pose, time, and style) expressive-feature model for representing and recognizing performance styles of human actions. The application of style analysis in computer animation for generating new animation styles of human motion have also been reported in @cite_6 @cite_58 @cite_49 @cite_63 @cite_80 @cite_66 .