Virtual Space Teleconferencing: Real-Time Reproduction of 3D Human Images

Ohya, Jun; Kitamura, Yasuichi; Kishino, Fumio; Terashima, Nobuyoshi; Takemura, Haruo; Ishii, Hirofumi

doi:10.1006/jvci.1995.1001

Cited by 58 publications

(12 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature-based approaches to facial deformation estimation extract discriminative information about the deformation of these facial elements from a discrete set of locations in the face. Initial feature-based approaches used make-up emphasised contours of eyebrows and lips [6], the corners of mouth, eyes and nostrils [28], colour markers [42] or the geometrical distribution of a set of fiducial points on the face [69]. Other approaches used a set of geometrical features on lips, eyebrows, cheek and furrow [59] or applied knowledge-based systems to reason about such features [46].…”

Section: Related Workmentioning

confidence: 99%

Recognising facial expressions in video sequences

Buenaposada

Muñoz

Baumela

2007

Pattern Anal Applic

View full text Add to dashboard Cite

We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89% recognition rate in a set of 333 sequences from the Cohn-Kanade data base.

show abstract

Section: Related Workmentioning

confidence: 99%

Recognising facial expressions in video sequences

Buenaposada

Muñoz

Baumela

2007

Pattern Anal Applic

View full text Add to dashboard Cite

show abstract

“…2 Although quite accurate, these cumbersome methods have limited applications. For this reason, ATR MIC developed passive methods to estimate facial expressions and body postures in real time using computer-vision-based techniques.…”

Section: Real-time Estimationmentioning

confidence: 99%

Virtual metamorphosis

Ohya¹,

Kurumisawa²,

Nakatsu³

et al. 1999

IEEE Multimedia

View full text Add to dashboard Cite

“…Ohya et al [44] from ATR Research Lab in Japan propose VISTEL (Virtual Space teleconferencing system). As the name indicates, the purpose of this system is to extend teleconferencing functionality into a virtual space where the participants can not only talk to each other and see each other, but collaborate in a 3D environment, sharing 3D objects to enhance their collaboration possibilities.…”

Section: Vistelmentioning

confidence: 99%