Gait recognition using HMMs and dual discriminative observations for sub-dynamics analysis

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.We propose a new gait recognition method that combines holistic and model-based features. Both types of features are extracted automatically from gait silhouette sequences and their combination takes place by means of a pair of hidden Markov models. In the proposed system, the holistic features are initially used for capturing general gait dynamics whereas, subsequently, the model-based features are deployed for capturing more detailed sub-dynamics by refining upon the preceding general dynamics. Furthermore, the holistic and model-based features are suitably processed in order to improve the discriminatory capacity of the final system. The experimental results show that the proposed method exhibits performance advantages in comparison with popular existing methods

Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series

Human Identification Using Gait

Keeping in view the growing importance of biometric signatures in automated security and surveillance systems, human gait recognition provides a low-cost non-obtrusive method for reliable person identification and is a promising area for research. This work employs a gait recognition process with binary silhouette-based input images and Hidden Markov Model (HMM)-based classification. The performance of the recognition method depends significantly on the quality of the extracted binary silhouettes. In this work, a computationally low-cost fuzzy correlogram based method is employed for background subtraction. Even highly robust background subtraction and shadow elimination algorithms produce erroneous outputs at times with missing body portions, which consequently affect the recognition performance. Frame Difference Energy Image (FDEI) reconstruction is performed to alleviate the detrimental effect of improperly extracted silhouettes and to make the recognition method robust to partial incompleteness. Subsequently, features are extracted via two methods and fed to the HMM based classifier which uses Viterbi decoding and Baum-Welch algorithm to compute similarity scores and carry out identification. The direct method uses extracted wavelet features directly for classification while the indirect method maps the higher-dimensional features into a lower dimensional space by means of a Frame-to-Exemplar-Distance (FED) vector. The FED uses the distance measure between pre-determined exemplars and the feature vectors of the current frame as an identification criterion. This work achieves an overall sensitivity of 86.44 % and 71.39 % using the direct and indirect approaches respectively. Also, variation in recognition performance is observed with change in the viewing angle and N and optimal performance is obtained when the path of subject parallel to camera axis (viewing angle of 0 degree) and at N = 5. The maximum recognition accuracy levels of 86.44 % and 80.93 % with and without FDEI reconstruction respectively also demonstrate the significance of FDEI reconstruction step

The Meaning of Action:a review on action recognition and mapping

In this paper, we analyze the different approaches taken to date within the computer vision, robotics and artificial intelligence communities for the representation, recognition, synthesis and understanding of action. We deal with action at different levels of complexity and provide the reader with the necessary related literature references. We put the literature references further into context and outline a possible interpretation of action by taking into account the different aspects of action recognition, action synthesis and task-level planning

3D Human Motion Tracking and Pose Estimation using Probabilistic Activity Models

This thesis presents work on generative approaches to human motion tracking and pose estimation where a geometric model of the human body is used for comparison with observations. The existing generative tracking literature can be quite clearly divided between two groups. First, approaches that attempt to solve a difficult high-dimensional inference problem in the body model’s full or ambient pose space, recovering freeform or unknown activity. Second, approaches that restrict inference to a low-dimensional latent embedding of the full pose space, recovering activity for which training data is available or known activity. Significant advances have been made in each of these subgroups. Given sufficiently rich multiocular observations and plentiful computational resources, highdimensional approaches have been proven to track fast and complex unknown activities robustly. Conversely, low-dimensional approaches have been able to support monocular tracking and to significantly reduce computational costs for the recovery of known activity. However, their competing advantages have – although complementary – remained disjoint. The central aim of this thesis is to combine low- and high-dimensional generative tracking techniques to benefit from the best of both approaches. First, a simple generative tracking approach is proposed for tracking known activities in a latent pose space using only monocular or binocular observations. A hidden Markov model (HMM) is used to provide dynamics and constrain a particle-based search for poses. The ability of the HMM to classify as well as synthesise poses means that the approach naturally extends to the modelling of a number of different known activities in a single joint-activity latent space. Second, an additional low-dimensional approach is introduced to permit transitions between segmented known activity training data by allowing particles to move between activity manifolds. Both low-dimensional approaches are then fairly and efficiently combined with a simultaneous high-dimensional generative tracking task in the ambient pose space. This combination allows for the recovery of sequences containing multiple known and unknown human activities at an appropriate (dynamic) computational cost. Finally, a rich hierarchical embedding of the ambient pose space is investigated. This representation allows inference to progress from a single full-body or global non-linear latent pose space, through a number of gradually smaller part-based latent models, to the full ambient pose space. By preserving long-range correlations present in training data, the positions of occluded limbs can be inferred during tracking. Alternatively, by breaking the implied coordination between part-based models novel activity combinations, or composite activity, may be recovered

Inferring Facial and Body Language

Machine analysis of human facial and body language is a challenging topic in computer vision, impacting on important applications such as human-computer interaction and visual surveillance. In this thesis, we present research building towards computational frameworks capable of automatically understanding facial expression and behavioural body language. The thesis work commences with a thorough examination in issues surrounding facial representation based on Local Binary Patterns (LBP). Extensive experiments with different machine learning techniques demonstrate that LBP features are efficient and effective for person-independent facial expression recognition, even in low-resolution settings. We then present and evaluate a conditional mutual information based algorithm to efficiently learn the most discriminative LBP features, and show the best recognition performance is obtained by using SVM classifiers with the selected LBP features. However, the recognition is performed on static images without exploiting temporal behaviors of facial expression. Subsequently we present a method to capture and represent temporal dynamics of facial expression by discovering the underlying low-dimensional manifold. Locality Preserving Projections (LPP) is exploited to learn the expression manifold in the LBP based appearance feature space. By deriving a universal discriminant expression subspace using a supervised LPP, we can effectively align manifolds of different subjects on a generalised expression manifold. Different linear subspace methods are comprehensively evaluated in expression subspace learning. We formulate and evaluate a Bayesian framework for dynamic facial expression recognition employing the derived manifold representation. However, the manifold representation only addresses temporal correlations of the whole face image, does not consider spatial-temporal correlations among different facial regions. We then employ Canonical Correlation Analysis (CCA) to capture correlations among face parts. To overcome the inherent limitations of classical CCA for image data, we introduce and formalise a novel Matrix-based CCA (MCCA), which can better measure correlations in 2D image data. We show this technique can provide superior performance in regression and recognition tasks, whilst requiring significantly fewer canonical factors. All the above work focuses on facial expressions. However, the face is usually perceived not as an isolated object but as an integrated part of the whole body, and the visual channel combining facial and bodily expressions is most informative. Finally we investigate two understudied problems in body language analysis, gait-based gender discrimination and affective body gesture recognition. To effectively combine face and body cues, CCA is adopted to establish the relationship between the two modalities, and derive a semantic joint feature space for the feature-level fusion. Experiments on large data sets demonstrate that our multimodal systems achieve the superior performance in gender discrimination and affective state analysis.Research studentship of Queen Mary, the International Travel Grant of the Royal Academy of Engineering, and the Royal Society International Joint Project