Infra-red pupil detection for use in a face recognition system

This paper presents a new method of eye localisation and face segmentation for use in a face recognition system. By using two near infrared light sources, we have shown that the face can be coarsely segmented, and the eyes can be accurately located, increasing the accuracy of the face localisation and improving the overall speed of the system. The system is able to locate both eyes within 25% of the eye-to-eye distance in over 96% of test cases.

An application of fractal image-set coding in facial recognition

Faces are complex patterns that often differ in only subtle ways. Face recognition algorithms have difficulty in coping with differences in lighting, cameras, pose, expression, etc. We propose a novel approach for facial recognition based on a new feature extraction method called fractal image-set encoding. This feature extraction method is a specialized fractal image coding technique that makes fractal codes more suitable for object and face recognition. A fractal code of a gray-scale image can be divided in two parts – geometrical parameters and luminance parameters. We show that fractal codes for an image are not unique and that we can change the set of fractal parameters without significant change in the quality of the reconstructed image. Fractal image-set coding keeps geometrical parameters the same for all images in the database. Differences between images are captured in the non-geometrical or luminance parameters – which are faster to compute. Results on a subset of the XM2VTS database are presented.

2D-3D Face Recognition Based on PCA and Feature Modelling

Hybrid face recognition, using image (2D) and structural (3D) information, has explored the fusion of Nearest Neighbour classifiers. This paper examines the effectiveness of feature modelling for each individual modality, 2D and 3D. Furthermore, it is demonstrated that the fusion of feature modelling techniques for the 2D and 3D modalities yields performance improvements over the individual classifiers. By fusing the feature modelling classifiers for each modality with equal weights the average Equal Error Rate improves from 12.60% for the 2D classifier and 12.10% for the 3D classifier to 7.38% for the Hybrid 2D+3D clasiffier.

A comparative analysis of major ERP lifecycle implementation, management and support issues in Queensland Government

This paper reports on a study of ERP lifecycle major issues from the perspectives of individuals with substantial and diverse involvement with SAP Financials in Queensland Government. A survey was conducted of 117 ERP system project participants in five closely related state government agencies. A modified Delphi technique identified, rationalized and weighed perceived major issues in ongoing ERP life cycle implementation, management and support. The five agencies each implemented SAP Financials simultaneously using a common implementation partner. The three survey rounds of the Delphi technique, together with coding and synthesizing procedures, resulted in a set of 10 major issue categories with 38 sub-issues. Relative scores of issue importance are compared across government agencies, roles (client vs implementation partner) and organizational levels (strategic, technical and operational). Study findings confirm the importance of this finer partitioning of the data, and distinctions identified reflect the circumstances of ERP lifecycle implementation, management and support among the stakeholder groups. The study findings should also be of interest to stakeholders who seek to better understand the issues surrounding ERP systems and to better realise the benefits of ERP.