Eng-Jon Ong

A boosted classifier tree for hand shape detection

The ability to detect a persons unconstrained hand in a natural video sequence has applications in sign language, gesture recognition and HCl. This paper presents a novel, unsupervised approach to training an efficient and robust detector which is capable of not only detecting the presence of human hands within an image but classifying the hand shape. A database of images is first clustered using a k-method clustering algorithm with a distance metric based upon shape context. From this, a tree structure of boosted cascades is constructed. The head of the tree provides a general hand detector while the individual branches of the tree classify a valid shape as belong to one of the predetermined clusters exemplified by an indicative hand shape. Preliminary experiments carried out showed that the approach boasts a promising 99.8% success rate on hand detection and 97.4% success at classification. Although we demonstrate the approach within the domain of hand shape it is equally applicable to other problems where both detection and classification are required for objects that display high variability in appearance.

Detection and tracking of humans by probabilistic body part assembly

This paper presents a probabilistic framework of assembling detected human body parts into a full 2D human configuration. The face, torso, legs and hands are detected in cluttered scenes using boosted body part detectors trained by AdaBoost. Body configurations are assembled from the detected parts using RANSAC, and a coarse heuristic is applied to eliminate obvious outliers. An a priori mixture model of upper-body configurations is used to provide a pose likelihood for each configuration. A joint-likelihood model is then determined by combining the pose, part detector and corresponding skin model likelihoods. The assembly with the highest likelihood is selected by RANSAC, and the elbow positions are inferred. This paper also illustrates the combination of skin colour likelihood and detection likelihood to further reduce false hand and face detections.

Sign language recognition using sub-units

This paper discusses sign language recognition using linguistic sub-units. It presents three types of sub-units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boosting to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%.

Viewpoint invariant exemplar-based 3D human tracking

This paper proposes a clustered exemplar-based model for performing viewpoint invariant tracking of the 3D motion of a human subject from a single camera. Each exemplar is associated with multiple view visual information of a person and the corresponding 3D skeletal pose. The visual information takes the form of contours obtained from different viewpoints around the subject. The inclusion of multi-view information is important for two reasons: viewpoint invariance; and generalisation to novel motions. Visual tracking of human motion is performed using a particle filter coupled to the dynamics of human movement represented by the exemplar-based model. Dynamics are modelled by clustering 3D skeletal motions with similar movement and encoding the flow both within and between clusters. Results of single view tracking demonstrate that the exemplar-based models incorporating dynamics generalise to viewpoint invariant tracking of novel movements.

Sign Language Recognition using Sequential Pattern Trees

This paper presents a novel, discriminative, multi-class classifier based on Sequential Pattern Trees. It is efficient to learn, compared to other Sequential Pattern methods, and scalable for use with large classifier banks. For these reasons it is well suited to Sign Language Recognition. Using deterministic robust features based on hand trajectories, sign level classifiers are built from sub-units. Results are presented both on a large lexicon single signer data set and a multi-signer Kinect™ data set. In both cases it is shown to out perform the non-discriminative Markov model approach and be equivalent to previous, more costly, Sequential Pattern (SP) techniques.

Robust Facial Feature Tracking Using Shape-Constrained Multiresolution-Selected Linear Predictors

This paper proposes a learned data-driven approach for accurate, real-time tracking of facial features using only intensity information. The task of automatic facial feature tracking is nontrivial since the face is a highly deformable object with large textural variations and motion in certain regions. Existing works attempt to address these problems by either limiting themselves to tracking feature points with strong and unique visual cues (e.g., mouth and eye corners) or by incorporating a priori information that needs to be manually designed (e.g., selecting points for a shape model). The framework proposed here largely avoids the need for such restrictions by automatically identifying the optimal visual support required for tracking a single facial feature point. This automatic identification of the visual context required for tracking allows the proposed method to potentially track any point on the face. Tracking is achieved via linear predictors which provide a fast and effective method for mapping pixel intensities into tracked feature position displacements. Building upon the simplicity and strengths of linear predictors, a more robust biased linear predictor is introduced. Multiple linear predictors are then grouped into a rigid flock to further increase robustness. To improve tracking accuracy, a novel probabilistic selection method is used to identify relevant visual areas for tracking a feature point. These selected flocks are then combined into a hierarchical multiresolution LP model. Finally, we also exploit a simple shape constraint for correcting the occasional tracking failure of a minority of feature points. Experimental results show that this method performs more robustly and accurately than AAMs, with minimal training examples on example sequences that range from SD quality to Youtube quality. Additionally, an analysis of the visual support consistency across different subjects is also provided.

Learning to Associate Faces across Views in Vector Space of Similarities to Prototypes

We present a method for learning appearance models that can be used to recognise and track both 3D head pose and identities of novel subjects with continuous head movement across the view-sphere. We describe an automatic face data acquisition system based on a magnetic sensor and a calibrated camera. The system enabled us to obtain systematically a database of face images with labelled 3D poses across a view-sphere of yaw and tilt at intervals of . The database was used to learn appearance models of unseen faces based on similarity measures to prototype faces. The method is computationally efficient and enables real-time performance.

Putting the pieces together: Connected Poselets for human pose estimation

We propose a novel hybrid approach to static pose estimation called Connected Poselets. This representation combines the best aspects of part-based and example-based estimation. First detecting poselets extracted from the training data; our method then applies a modified Random Decision Forest to identify Poselet activations. By combining keypoint predictions from poselet activitions within a graphical model, we can infer the marginal distribution over each keypoint without any kinematic constraints. Our approach is demonstrated on a new publicly available dataset with promising results.

Robust facial feature tracking using selected multi-resolution linear predictors

This paper proposes a learnt data-driven approach for accurate, real-time tracking of facial features using only intensity information. Constraints such as a-priori shape models or temporal models for dynamics are not required or used. Tracking facial features simply becomes the independent tracking of a set of points on the face. This allows us to cope with facial configurations not present in the training data. Tracking is achieved via linear predictors which provide a fast and effective method for mapping pixel-level information to tracked feature position displacements. To improve on this, a novel and robust biased linear predictor is proposed in this paper. Multiple linear predictors are grouped into a rigid flock to increase robustness. To further improve tracking accuracy, a novel probabilistic selection method is used to identify relevant visual areas for tracking a feature point. These selected flocks are then combined into a hierarchical multi-resolution LP model. Experimental results also show that this method performs more robustly and accurately than AAMs, without any a priori shape information and with minimal training examples.

Learning Sequential Patterns for Lipreading

This paper proposes a novel machine learning algorithm (SP-Boosting) to tackle the problem of lipreading by building visual sequence classifiers based on s equential patterns. We show that an exhaustive search of optimal sequential patterns is not possible due to the immense search space, and tackle this with a novel, efficient tr ee-search method with a set of pruning criteria. Crucially, the pruning strategies pres erve our ability to locate the optimal sequential pattern. Additionally, the tree-based searc h method accounts for the training set’s boosting weight distribution. This temporal s earch method is then integrated into the boosting framework resulting in the SP-Boosting algorithm. We also propose a novel constrained set of strong classifiers that fur ther improves recognition accuracy. The resulting learnt classifiers are applied to lipreading b y performing multi-class recognition on the OuluVS database. Experimental results show that our method achieves state of the art recognition performane, using only a small set of sequential patterns.