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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Face To Face Communications In Multiplayer Online Games: A Real-Time System, Ce Zhan, Wanqing Li, Farzad Safaei, Philip Ogunbona
Face To Face Communications In Multiplayer Online Games: A Real-Time System, Ce Zhan, Wanqing Li, Farzad Safaei, Philip Ogunbona
Professor Philip Ogunbona
Multiplayer online games (MOG) bring HCI into a new era of human-human interactions in computer world. Although current MOG provide more interactivity and social interaction in the virtual world, natural facial expression as a key factor in emulating face to face communications has been neglected by game designers. In this work, we propose a real-time automatic system to recognize players’ facial expressions, so that the recognition results can be used to drive the MOG’s “facial expression engine” instead of “text commands”. Our major contributions are the evaluation, improvement and efficient implementation of existing algorithms to build a real-time system that …
Finding Distinctive Facial Areas For Face Recognition, Ce Zhan, Wanqing Li, Philip O. Ogunbona
Finding Distinctive Facial Areas For Face Recognition, Ce Zhan, Wanqing Li, Philip O. Ogunbona
Professor Philip Ogunbona
One of the key issues for local appearance based face recognition methods is that how to find the most discriminative facial areas. Most of the existing methods take the assumption that anatomical facial components, such as the eyes, nose, and mouth, are the most useful areas for recognition. Other more elaborate methods locate the most salient parts within the face according to a pre-specified criterion. In this paper, a novel method is proposed to identify the discriminative facial areas for face recognition. Unlike the existing methods that only analyze the given face, the proposed method identifies the distinctive areas of …
Illumination Invariant Face Detection Using Classifier Fusion, Alister Cordiner, Philip Ogunbona, Wanqing Li
Illumination Invariant Face Detection Using Classifier Fusion, Alister Cordiner, Philip Ogunbona, Wanqing Li
Professor Philip Ogunbona
An approach to the problem of illumination variations in face detection that uses classifier fusion is presented. Multiple face detectors are seperately trained for different illumination environments and their results are combined using a combination rule. To define the illumination environments, the training samples are clustered based on their illumination using unsupervised training. Different methods of clustering the samples and combining the outputs of the classifiers are examined. Experiments with the AR face database show that the proposed method achieves higher accuracy than the traditional monolithic face detection method.
Face Recognition From Single Sample Based On Human Face Perception, Ce Zhan, Wanqing Li, Philip Ogunbona
Face Recognition From Single Sample Based On Human Face Perception, Ce Zhan, Wanqing Li, Philip Ogunbona
Professor Philip Ogunbona
Although research show that human recognition performance for unfamiliar faces is relatively poor, when the sample is always available for analysis and becomes ”familiar”, people are able to recognize a previous unknown face from single sample. In this paper, a method is proposed to deal with the one sample per person face recognition problem based on the process how unfamiliar faces become familiar to people. Particularly, quantized local features which learnt from generic face dataset are used in the proposed method to mimic the prototype effect of human face recognition. Furthermore, a landmark-based scheme is introduced to quantify the distinctiveness …