To reconstruct low-resolution facial photographs which are in focus and without motion blur, a novel algorithm based on local similarity preserving is proposed. It is based on the theories of local manifold learning. The innovations of the new method include mixing point-based entropy and Euclidian distance to search for the nearest points, adding point-to-patch degradation model to restrict the linear weights and compensating the fusing patch to keep energy coherence. The compensation reduces the algorithm dependence on training sets and keeps the luminance of reconstruction constant. Experiments show that our method can effectively reconstruct 1612 images with the magnification of 88 and the 3224 facial photographs in focus and without motion blur.

This paper describes a method of age-group classification of young males based on their facial images. The facial shapes of males and females are mostly formed by age 20 and 15, respectively. Our study only considered young males as they have a longer period during which facial shape is a determining factor in age estimation. Age classification was carried out using artificial neural networks. We employed 440 facial images in our experiment, composed of 4 different photographic images taken at ages 12, 15, 18 and 22 of 110 young males. Two methods of age classification were used, each employing different features extracted from the facial images, namely, "mosaic features" and "KL features. " As a result, we obtained about an 80% successful classification rate using mosaic features, and a slightly lower rate using KL features. We also analyzed the connection weights between the hidden and input layers of the trained networks, and examined facial features characteristic to each age group.

Muscle based face image synthesis is one of the most realistic approaches to the realization of a life-like agent in computers. A facial muscle model is composed of facial tissue elements and simulated muscles. In this model, forces are calculated effecting a facial tissue element by contraction of each muscle string, so the combination of each muscle contracting force decides a specific facial expression. This muscle parameter is determined on a trial and error basis by comparing the sample photograph and a generated image using our Muscle-Editor to generate a specific face image. In this paper, we propose the strategy of automatic estimation of facial muscle parameters from 2D markers'movements located on a face using a neural network. This corresponds to the non-realtime 3D facial motion capturing from 2D camera image under the physics based condition.

This paper proposes and investigates an intelligent error-controlling scheme according to different importance of segmental information. In particular, the scheme is designed for facial images encoded by model-based coding that is a kind of intelligent compression coding. Intelligent communication systems regard the contents of information to be transmitted with extremely high compression and reliability. After highly efficient information compression by model-beaed coding, errors in the compressed information lead to severe semantic errors. The proposed scheme reduces semantic errors of information for the receiver. In this paper, we consider Action Unit (AU) as a segment of model-based coded facial image of human being and define the importance for each AU. According to the importance, an AU is encoded by an appropriated code among codes with different error-correcting capabilities. For encoding with different error controlling codes, we use three kinds of constructions to obtain unequal error protection (UEP) codes in this paper. One of them is the direct sum construction and the others are the proposed constructions which are based on joint and double coding. These UEP codes can have higher coderate than other UEP codes when minimum Hamming distance is small. By using these UEP codes, the proposed intelligent error-controlling scheme can protect information in segment in order to reduce semantic errors over a conventional error-controlling scheme in which information is uniformly protected by an error-correcting code.

Recently, studies aiming at the next generation of visual communication services which support better human communication have been carried out intensively in Japan. The principal motive of these studies is to develop new services which are not restricted to a conventional communication framework based on the transmission of waveform signals. This paper focuses on three important key words in these studies; "intelligent," "real," and "distributed and collaborative," and describes recent research activities. The first key word "intelligent" relates to intelligent image coding. As a particular example, model-based coding of moving facial images is discussed in detail. In this method, shape change and motion of the human face is described by a small number of parameters. This feature leads to the development of new applications such as very low bit-rate transmission of moving facial images, analysis and synthesis of facial expression, human interfaces, and so on. The second key word "real" relates to communication with realistic sensations and virtual space teleconferencing. Among various component technologies, real-time reproduction of 3-D human images and a cooperative work environment with virtual space are discussed in detail. The last key word "distributed and collaborative" relates to collaborative work in a distributed work environment. The importance of visual media in collaborative work, a concept of CSCW, and requirements for realizing a distributed collaborative environment are discussed. Then, four examples of CSCW systems are briefly outlined.

Despite its potential to realize image communication at extremely low rates, model-based coding (analysis-synthesis coding) still has problems to be solved for any practical use. The main problems are the difficulty in modeling unknown objects and the presence of analysis errors. To cope with these difficulties, we incorporate waveform coding into model-based coding (model-based/waveform hybrid coding). The incorporated waveform coder can code unmodeled objects and cancel the artifacts caused by the analysis errors. From a different point of view, the performance of the practically used waveform coder can be improved by the incorporation of model-based coding. Since the model-based coder codes the modeled part of the image at extremely low rates, more bits can be allocated for the coding of the unmodeled region. In this paper, we present the basic concept of model-based/waveform hybrid coding. We develop a model-based/MC-DCT hybrid coding system designed to improve the performance of the practically used MC-DCT coder. Simulation results of the system show that this coding method is effective at very low transmission rates such as 16kb/s. Image transmission at such low rates is quite difficult for an MC-DCT coder without the contribution of the model-based coder.

This paper presents an automatic creation method of 3D facial models which are needed for facial image generation by 3D computer graphics. A 3D facial model of a specific person is obtained from just the front and side view images without any human operation. The method has two parts; feature extraction and generic model modification. In the feature extraction part, the regions or edges which express the facial features such as eyes, nose, mouth or chin outline are extracted from the front and side view images. A generic head model is then modified based on the position and shape of the extracted facial features in the generic model modification part. As a result, a 3D model for persons is obtained. By using the specific model and the front and side view images, texture-mapped facial images can be generated easily.