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Full-Text Articles in Physical Sciences and Mathematics
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 …
Facial Expression Recognition For Multiplayer Online Games, Ce Zhan, Wanqing Li, Philip O. Ogunbona, Farzad Safaei
Facial Expression Recognition For Multiplayer Online Games, Ce Zhan, Wanqing Li, Philip O. Ogunbona, Farzad Safaei
Professor Philip Ogunbona
The Multiplayer Online Game (MOG) becomes more popular than any other types of computer games for its collaboration, communication and interaction ability. However, compared with the ordinary human communication, the MOG still has many limitations, especially in communication using facial expressions. Although detailed facial animation has already been achieved in a number of MOGs, players have to use text commands to control avatars expressions. In this paper, we briefly review the state of the art in facial expression recognition and propose an automatic expression recognition system that can be integrated into a MOG to control the avatar’s facial expressions. We …
On The Combination Of Local Texture And Global Structure For Food Classification, Zhimin Zong, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
On The Combination Of Local Texture And Global Structure For Food Classification, Zhimin Zong, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
Professor Philip Ogunbona
This paper proposes a food image classification method using local textural patterns and their global structure to describe the food image. In this paper, a visual codebook of local textural patterns is created by employing Scale Invariant Feature Transformation (SIFT) interest point detector with the Local Binary Pattern (LBP) feature. In addition to describing the food image using local texture, the global structure of the food object is represented as the spatial distribution of the local textural structures and encoded using shape context. We evaluated the proposed method on the Pittsburgh Fast-Food Image (PFI) dataset. Experimental results showed that the …