Using facial feature extraction to enhance the creation of 3D human models

The creation of personalised 3D characters has evolved to provide a high degree of realism in both appearance and animation. Further to the creation of generic characters the capabilities exist to create a personalised character from images of an individual. This provides the possibility of immersing an individual into a virtual world. Feature detection, particularly on the face, can be used to greatly enhance the realism of the model. To address this innovative contour based templates are used to extract an individual from four orthogonal views providing localisation of the face. Then adaptive facial feature extraction from multiple views is used to enhance the realism of the model

A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers

Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation

Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions

Automated Markerless Extraction of Walking People Using Deformable Contour Models

We develop a new automated markerless motion capture system for the analysis of walking people. We employ global evidence gathering techniques guided by biomechanical analysis to robustly extract articulated motion. This forms a basis for new deformable contour models, using local image cues to capture shape and motion at a more detailed level. We extend the greedy snake formulation to include temporal constraints and occlusion modelling, increasing the capability of this technique when dealing with cluttered and self-occluding extraction targets. This approach is evaluated on a large database of indoor and outdoor video data, demonstrating fast and autonomous motion capture for walking people

3D Model Assisted Image Segmentation

The problem of segmenting a given image into coherent regions is important in Computer Vision and many industrial applications require segmenting a known object into its components. Examples include identifying individual parts of a component for proces

From 3D Point Clouds to Pose-Normalised Depth Maps

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)

Flame front analysis of ethanol, butanol, iso-octane and gasoline in a spark-ignition engine using laser tomography and integral length scale measurements

Direct-injection spark-ignition engines have become popular due to their flexibility in injection strategies and higher efficiency; however, the high-pressure in-cylinder injection process can alter the airflow field by momentum exchange, with different effects for fuels of diverse properties. The current paper presents results from optical studies of stoichiometric combustion of ethanol, butanol, iso-octane and gasoline in a direct-injection spark-ignition engine run at 1500 RPM with 0.5 bar intake plenum pressure and early intake stroke fuel injection for homogeneous mixture preparation. The analysis initially involved particle image velocimetry measurements of the flow field at ignition timing with and without fuelling for comparison. Flame chemiluminescence imaging was used to characterise the global flame behaviour and double-pulsed Laser-sheet flame tomography by Mie scattering to quantify the local topology of the flame front. The flow measurements with fuel injection showed integral length scales of the same order to those of air only on the tumble plane, but larger regions with scales up to 9 mm on the horizontal plane. Averaged length scales over both measurement planes were between 4 and 6 mm, with ethanol exhibiting the largest and butanol the smallest. In non-dimensional form, the integral length scales were up to 20% of the clearance height and 5–12% of the cylinder bore. Flame tomography showed that at radii between 8 and 12 mm, ethanol was burning the fastest, followed by butanol, iso-octane and gasoline. The associated turbulent burning velocities were 4.6–6.5 times greater than the laminar burning velocities and about 13–20% lower than those obtained by flame chemiluminescence imaging. Flame roundness was 10–15% on the tomography plane, with largest values for ethanol, followed by butanol, gasoline and iso-octane; chemiluminescence imaging showed larger roundness (18–25%), albeit with the same order amongst fuels. The standard deviation of the displacement of the instantaneous flame contour from one filtered by its equivalent radius was obtained as a measure of flame brush thickness and correlated strongly with the equivalent flame radius; when normalised by the radius, it was 4–6% for all fuels. The number of crossing points between instantaneous and filtered flame contour showed a strong negative correlation with flame radius, independent of fuel type. The crossing point frequency was 0.5–1.6 mm−1. The flame brush thickness was about 1/10th of the integral length scale. A positive correlation was found between integral length scale and flame brush thickness and a negative correlation with crossing frequency