Temporal-based support vector machines for facial expression recognition

When classifying a signal, ideally we want our classifier to trigger a large response when it encounters a positive example and have little to no response for all other examples. Unfortunately in practice this does not occur with responses fluctuating, often causing false alarms. There exists a myriad of reasons why this is the case, most notably not incorporating the dynamics of the signal into the classification. In facial expression recognition, this has been highlighted as one major research question. In this paper we present a novel technique which incorporates the dynamics of the signal which can produce a strong response when the peak expression is found and essentially suppresses all other responses as much as possible. We conducted preliminary experiments on the extended Cohn-Kanade (CK+) database which shows its benefits. The ability to automatically and accurately recognize facial expressions of drivers is highly relevant to the automobile. For example, the early recognition of “surprise” could indicate that an accident is about to occur; and various safeguards could immediately be deployed to avoid or minimize injury and damage. In this paper, we conducted initial experiments on the extended Cohn-Kanade (CK+) database which shows its benefits.

Modelling of inflatable rescue boats (IRBs) in surf conditions to reduce injuries

The Inflatable Rescue Boat (IRB) is arguably the most effective rescue tool used by the Australian surf lifesavers. The exceptional features of high mobility and rapid response have enabled it to become an icon on Australia's popular beaches. However, the IRB's extensive use within an environment that is as rugged as it is spectacular, has led it to become a danger to those who risk their lives to save others. Epidemiological research revealed lower limb injuries to be predominant, particularly the right leg. The common types of injuries were fractures and dislocations, as well as muscle or ligament strains and tears. The concern expressed by Surf Life Saving Queensland (SLSQ) and Surf Life Saving Australia (SLSA) led to a biomechanical investigation into this unique and relatively unresearched field. The aim of the research was to identify the causes of injury and propose processes that may reduce the instances and severity of injury to surf lifesavers during IRB operation. Following a review of related research, a design analysis of the craft was undertaken as an introduction to the craft, its design and uses. The mechanical characteristics of the vessel were then evaluated and the accelerations applied to the crew in the IRB were established through field tests. The data were then combined and modelled in the 3-D mathematical modelling and simulation package, MADYMO. A tool was created to compare various scenarios of boat design and methods of operation to determine possible mechanisms to reduce injuries. The results of this study showed that under simulated wave loading the boats flex around a pivot point determined by the position of the hinge in the floorboard. It was also found that the accelerations experienced by the crew exhibited similar characteristics to road vehicle accidents. Staged simulations indicated the attributes of an optimum foam in terms of thickness and density. Likewise, modelling of the boat and crew produced simulations that predicted realistic crew response to tested variables. Unfortunately, the observed lack of adherence to the SLSA footstrap Standard has impeded successful epidemiological and modelling outcomes. If uniformity of boat setup can be assured then epidemiological studies will be able to highlight the influence of implementing changes to the boat design. In conclusion, the research provided a tool to successfully link the epidemiology and injury diagnosis to the mechanical engineering design through the use of biomechanics. This was a novel application of the mathematical modelling software MADYMO. Other craft can also be investigated in this manner to provide solutions to the problem identified and therefore reduce risk of injury for the operators.

What factors are associated with cyclists getting injured? Correlates of cyclist injuries in Queensland

Pedal cyclists are over-represented in traffic crash injuries in Australia. This study examined correlates of cycling injuries in a sample of Queensland cyclists. Members of Bicycle Queensland (n=1976) were asked about cycling injuries as part of an online survey. They also reported demographic characteristics, reasons for cycling, years of cycling as an adult, and cycling frequency. Multivariate logistic regression modelling was used to examine the association between these variables and experiencing cycling injuries last year (yes/no). Thirty-one percent of respondents (n=617) reported at least one cycling injury. Respondents had greater likelihood of injury if they cycled more frequently, had cycled <5 years, or cycled for recreation or competition. These findings suggest that injuries are mostly likely to occur among less experienced cyclists, those cycling the most, and those cycling for sport and recreation. Injury prevention interventions should include cycle skills training along with fostering safer cycling environments.

Creating an effective prevention curriculum to reduce transport risks and injuries

Adolescent injury is a significant health concern and can be a result of the adolescents engagement in transport-related behaviours. There is however significant planning and formative research needed to inform prevention programme design. This presentation reports on the development and evaluation of a curriculum programme that was shown to be effective in reducing transport-related risks and injuries. Early adolescents report injuries resulting from a number of transport-related behaviours including those associated with riding a bicycle, a motorcycle, and as a passenger (survey of 209 Year 9 students). In focus groups, students (n=30) were able to describe the context of transport risks and injuries. Such information provided evidence of the need for an intervention and ecologically valid data on which to base programme design including insights into the language, culture and development of adolescents and their experiences with transport risks. Additional information about teaching practices and implementation issues were explored in interviews with 13 teachers. A psychological theory was selected to operationalise the design of the programmes that drew on such preparatory data. The programme, Skills for Preventing Injury in Youth was evaluated with 197 participating and 137 control students (13–14 year olds). Results showed a significant difference between the intervention and control groups from baseline to 6-month follow-up in a number of transport-related risk behaviours and transport-related injuries. The programme thus demonstrated potential in reduce early adolescents transport risk behaviours and associated harm. Discussion will involve the implications of the development research process in designing road safety interventions.

Person-independent facial expression detection using constrained local models

In automatic facial expression detection, very accurate registration is desired which can be achieved via a deformable model approach where a dense mesh of 60-70 points on the face is used, such as an active appearance model (AAM). However, for applications where manually labeling frames is prohibitive, AAMs do not work well as they do not generalize well to unseen subjects. As such, a more coarse approach is taken for person-independent facial expression detection, where just a couple of key features (such as face and eyes) are tracked using a Viola-Jones type approach. The tracked image is normally post-processed to encode for shift and illumination invariance using a linear bank of filters. Recently, it was shown that this preprocessing step is of no benefit when close to ideal registration has been obtained. In this paper, we present a system based on the Constrained Local Model (CLM) which is a generic or person-independent face alignment algorithm which gains high accuracy. We show these results against the LBP feature extraction on the CK+ and GEMEP datasets.

Toward a more robust facial expression recognition in occluded images using randomly sampled Gabor based templates

Occlusion is a big challenge for facial expression recognition (FER) in real-world situations. Previous FER efforts to address occlusion suffer from loss of appearance features and are largely limited to a few occlusion types and single testing strategy. This paper presents a robust approach for FER in occluded images and addresses these issues. A set of Gabor based templates is extracted from images in the gallery using a Monte Carlo algorithm. These templates are converted into distance features using template matching. The resulting feature vectors are robust to occlusion. Occluded eyes and mouth regions and randomly places occlusion patches are used for testing. Two testing strategies analyze the effects of these occlusions on the overall recognition performance as well as each facial expression. Experimental results on the Cohn-Kanade database confirm the high robustness of our approach and provide useful insights about the effects of occlusion on FER. Performance is also compared with previous approaches.

Frequency decomposition techniques for increased discriminative 3D facial information capture

Eigen-based techniques and other monolithic approaches to face recognition have long been a cornerstone in the face recognition community due to the high dimensionality of face images. Eigen-face techniques provide minimal reconstruction error and limit high-frequency content while linear discriminant-based techniques (fisher-faces) allow the construction of subspaces which preserve discriminatory information. This paper presents a frequency decomposition approach for improved face recognition performance utilising three well-known techniques: Wavelets; Gabor / Log-Gabor; and the Discrete Cosine Transform. Experimentation illustrates that frequency domain partitioning prior to dimensionality reduction increases the information available for classification and greatly increases face recognition performance for both eigen-face and fisher-face approaches.

The impact of school connectedness on violent behavior, transport risk taking behavior and associated injuries in adolescence

Adolescents engage in many risk-taking behaviors that have the potential to lead to injury. The school environment has a significant role in shaping adolescent behavior, and this study aimed to provide additional information about the benefits associated with connectedness to school. Early adolescents aged 13 to 15 years (N = 509, 49% boys) were surveyed about school connectedness, engagement in transport and violence risk-taking, and injury experiences. Significant relations were found between school connectedness and reduced engagement in both transport and violence risk-taking, as well as fewer associated injuries. This study has implications for the area of risk-taking and injury prevention, as it suggests the potential for reducing adolescents' injury through school based interventions targeting school connectedness.

Injuries across adolescence : an investigation using the Extended Adolescent Injury Checklist (E-AIC)

Injury is the leading cause of death among adolescents, and in many countries, accounts for more deaths than all other causes combined. Rates of death due to injury also increase dramatically across adolescence. The Australian Institute of Health and Welfare reported that, in 2005, there were 954 deaths of young Australians due to injury, which is a rate of 26 deaths per 100,000 young people. Of these deaths, 4% were adolescents aged 12-14, 17% were aged 15-17, and 80% were aged 18-24 years. Issues addressed: Injuries are the leading cause of death among adolescents. The current research examined a measure of adolescent injury in terms of whether it encompasses the diverse injury experiences of Australian adolescents, including high-risk and normative adolescents, and thus determine its utility as a tool for health promotion research. Grade 9 students from two Brisbane high schools (n=202, aged 13-14 years) and adolescents recruited from the Emergency Department waiting rooms of four Brisbane hospitals (n=98, aged 16-18 years) completed the Extended Adolescent Injury Checklist (E-AIC). The most common cause of injury among adolescents was a sports activity, followed by fights for all participants except schoolbased males, who experienced more bicycle injuries. Alcohol use was most frequently reported in association with interpersonal violence injuries. A broad variety of injuries, occurring in context of multiple risk as well as normative behaviours, were reported by adolescents in both school and ED settings, and were captured by the E-AIC. Findings suggest that the E-AIC is a useful measure that captures the injury experiences of adolescents in different contexts. The high occurrence of injuries that do not result in formal medical treatment also indicates scope for interventions to be based around lessons in first aid, while also incorporating injury prevention components.

Incidence, severity and correlates of bicycling injuries in a sample of cyclists in Queensland, Australia

Bicycle injuries, particularly those resulting from single bicycle crashes, are underreported in both police and hospital records. Data on cyclist characteristics and crash circumstances are also often lacking. As a result, the ability to develop comprehensive injury prevention policies is hampered. The aim of this study was to examine the incidence, severity, cyclist characteristics, and crash circumstances associated with cycling injuries in a sample of cyclists in Queensland, Australia. A cross-sectional study of Queensland cyclists was conducted in 2009. Respondents (n=2056) completed an online survey about their cycling experiences, including cycling injuries. Logistic regression modelling was used to examine the associations between demographic and cycling behaviour variables with experiencing cycling injuries in the past year, and, separately, with serious cycling injuries requiring a trip to a hospital. Twenty-seven percent of respondents (n=545) reported injuries, and 6% (n=114) reported serious injuries. In multivariable modelling, reporting an injury was more likely for respondents who had cycled <5 years, compared to ≥10 years (p<0.005); cycled for competition (p=0.01); or experienced harassment from motor vehicle occupants (p<0.001). There were no gender differences in injury incidence, and respondents who cycled for transport did not have an increased risk of injury. Reporting a serious injury was more likely for those whose injury involved other road users (p<0.03). Along with environmental and behavioural approaches for reducing collisions and near-collisions with motor vehicles, interventions that improve the design and maintenance of cycling infrastructure, increase cyclists’ skills, and encourage safe cycling behaviours and bicycle maintenance will also be important for reducing the overall incidence of cycling injuries.

Facial expression recognition using facial movement features

Facial expression is an important channel for human communication and can be applied in many real applications. One critical step for facial expression recognition (FER) is to accurately extract emotional features. Current approaches on FER in static images have not fully considered and utilized the features of facial element and muscle movements, which represent static and dynamic, as well as geometric and appearance characteristics of facial expressions. This paper proposes an approach to solve this limitation using ‘salient’ distance features, which are obtained by extracting patch-based 3D Gabor features, selecting the ‘salient’ patches, and performing patch matching operations. The experimental results demonstrate high correct recognition rate (CRR), significant performance improvements due to the consideration of facial element and muscle movements, promising results under face registration errors, and fast processing time. The comparison with the state-of-the-art performance confirms that the proposed approach achieves the highest CRR on the JAFFE database and is among the top performers on the Cohn-Kanade (CK) database.

Improving the performance of facial expression recognition using dynamic, subtle and regional features

Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based ‘salient’ Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to the use of the dynamic features. Performance comparison with pervious work also confirms that the proposed approach achieves the highest CRR reported to date on the JAFFE database and a top-level performance on the Cohn-Kanade (CK) database.