Realtime parallel and cooperative recognition of human face for a naturalistic visual human interface

Hasegawa, Osamu; Ishizuka, Mitsuru; Yokosawa, Kazuhiko

doi:10.1002/scj.4690251102

Cited by 4 publications

(2 citation statements)

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“…Then we can obtain the new edge image G2u'2)(x, y) such that G2u)2)(x, y) = Vg2'2)(x,y) = 1 if 2W2)(T y))2 + (g2U2)(x, y))2 > T2 and {G11)(x,y) = 1 : (x,y) E S} = 0 else, (6) where, T2 is the root mean value of {(g2'2)(x. y))2 + (g2L2)(x, y))2} over the total image area. S is the 8 neighbour pixels of each edge pixel at (x. y) found in the previous step.…”

Section: Edge Detectionmentioning

confidence: 99%

<title>Detection and tracking of facial features</title>

1995

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Detection and tracking of facial features without using any head mounted devices may become required in various future visual comnnuiication applications, such as teleconferencing, virtual reality etc. In this paper W( propose an automatic iiietliod of face featiue detection using a method called edge pixel counting. Instead of utilizing color or gray scale information of the facial image, the proposed edge pixel counting method utilizes the edge information to estunate the face feature positions such as eyes, nose and mouth in the first frame of a moving facial image sequence, using a variable size face feature template. For the remaining frames. feature tracking is carried out alternatively using a method called deforinable template rnatching and edge pixel counting. One main advantage of using edge pixel counting iii feature tracking is that it does not require the condition of a high iiiter frame correlation around the feature areas as is required in template matching. Some expeiiineiital results are shown to demonstrate the effectiveness of the proposed method.

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Section: Edge Detectionmentioning

confidence: 99%

<title>Detection and tracking of facial features</title>

1995

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“…The interest paid to attention has grown a lot recently and is being used in real-world applications. Some implemented systems based on selective attention have so far covered up several of the following categories: recognition [4,19,32,36,40], teleconferencing [22], tracking of multiple objects [5,39], and mobile robot navigation [2,3,42,29,6]. …”

Section: Introduction To Selective Attentionmentioning

confidence: 99%

Dynamic visual attention model in image sequences

López

Fernández

Fernández-Caballero

et al. 2007

Image and Vision Computing

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A new computational architecture of dynamic visual attention is introduced in this paper. Our approach defines a model for the generation of an active attention focus on a dynamic scene captured from a still or moving camera. The aim is to obtain the objects that keep the observer's attention in accordance with a set of predefined features, including color, motion and shape. The solution proposed to the selective visual attention problem consists in decomposing the input images of an indefinite sequence of images into its moving objects, by defining which of these elements are of the user's interest, and by keeping attention on those elements through time. Thus, the three tasks involved in the attention model are introduced. The Feature-Extraction task obtains those features (color, motion and shape features) necessary to perform object segmentation. The Attention-Capture task applies the criteria established by the user (values provided through parameters) to the extracted features and obtains the different parts of the objects of potential interest. Lastly, the Attention-Reinforcement task maintains attention on certain elements (or objects) of the image sequence that are of real interest.

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Dynamic display of CG facial expressions for active human interface

Aritake

Hara

Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication

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In order to develop "Active Human Interface" that realizes heart-to-heart communication between intelligent machine and human being, we've bcen undertaking the investigation of method for improving the sensibility or "KAh'SEI" communication by using facial expressions. To express 6 basic facial expressions dynamically by computer graphics(CG) in a similar manner to that in human being,, we develop the method of dynamic control of the movement of grid points of a wire frame model of human face by applying a neural network. Using the experimental data obtained from dynamic fiicial expressions, time-dependent change in the magnitude of AUs needed for expressing each of 6 basic facial expressions is formulated by the NN mapping technique. And the face components in the wire frame model are dynamically forcpd to move through the 'time-dependent AUs, resulting in success to display the dynamic facial expression of 6 basic eniotions at the rate of 30 pictures per second. '

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Realtime parallel and cooperative recognition of human face for a naturalistic visual human interface

Cited by 4 publications

References 4 publications

<title>Detection and tracking of facial features</title>

<title>Detection and tracking of facial features</title>

Dynamic visual attention model in image sequences

Dynamic display of CG facial expressions for active human interface

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