Face recognition robust to head pose from one sample image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.

A comparison of responses to group learning between first-year Asian and first-year Australian veterinary science students

Introduction - Group learning has been used to enhance deep (long-term) learning and promote life skills, such as decision making, communication, and interpersonal skills. However, with increasing multiculturalism in higher education, there is little information available as to the acceptance of this form of learning by Asian students or as to its value to them. Methodology - Group-learning projects, incorporating a seminar presentation, were used in first-year veterinary anatomical science classes over two consecutive years (2003 and 2004) at the School of Veterinary Science, University of Queensland. Responses of Australian and Asian students to survey forms evaluating the learning experience were analyzed and compared. Results - All students responded positively to the group learning, indicating that it was a useful learning experience and a great method for meeting colleagues. There were no significant differences between Asian and Australian students in overall responses to the survey evaluating the learning experience, except where Asian students responded significantly higher than Australian students in identifying specific skills that needed improving. Conclusions - Group learning can be successfully used in multicultural teaching to enhance deep learning. This form of learning helps to remove cultural barriers and establish a platform for continued successful group learning throughout the program.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.