Mamoru Yasumoto

Focus of attention for face and hand gesture recognition using multiple cameras

We propose a multi-camera system that can track multiple human faces and hands as well as focus on face and hand gestures for recognition. Our current system consists of four cameras. Two fixed cameras are used as a stereo system to estimate face and hand positions. The stereo camera detects faces and hands by a standard skin color method we propose. The distances of the targets are then estimated. Next to track multiple targets, we estimate the positions and sizes of targets between consecutive frames. The other two cameras perform tracking of such targets as faces and hands. If a target is not the appropriate size for recognition, the tracking cameras acquire its zoomed image. Since our system has two tracking cameras, it can track two targets at the same time. To recognize faces and hand gestures, we propose four directional features by using linear discriminant analysis. Using our system, we experimented on human position estimation, multiple face tracking, and face and hand gesture recognition. These experiments showed that our system could estimate human position with the stereo camera and track multiple targets by using target positions and sizes even if the persons overlapped with each other. In addition, our system could recognize faces and hand gestures by using the four directional features.

Age and gender estimation from facial image processing

We are researching about an age and gender estimation based on wrinkle texture and color of facial images. A preliminary questionnaire (enquete) examination on how effectively the facial images could be used for gender and age estimations was executed by using 300 different faces and 21 examinees. Wrinkles appearing in the face and the shape and size of the facial parts are selected to model the age and gender estimation in this research basing on this enquete. Basing on this preliminary consideration, an image-processing algorithm for wrinkle modeling, was proposed. In addition, a method for making relationships between facial images and their keywords was proposed by using the latent semantic indexing. An efficient interface for displaying the relationships among keywords and facial images has been introduced with 2D/3D-feature space defined by the indexing.

Face and hand gesture recognition for human-computer interaction

We propose a multi-camera system that can focus on faces and hand gestures for recognition. The system consists of four cameras. Two fixed cameras are used as a stereo system to estimate face and hand (F&H) positions. F&H regions are detected by a standard skin color method. The distances of the targets are then estimated. The positions and sizes of targets are evaluated for tracking between consecutive frames. The other two cameras perform tracking of such targets as faces and hands. If a target is not the appropriate size for recognition, the tracking cameras acquire its zoomed image. We propose a hierarchy linear discriminant analysis that distinguishes between faces and hands on the first step, and then recognizes face and hand gestures in each discriminant space by using four directional features. The experimental results show that our system could distinguish between faces and hands, as well as recognize face and hand gestures by using the four directional features.

Age and gender estimation based on wrinkle texture and color of facial images

We are researching about an age and gender estimation based on wrinkle texture and color of facial images. Preliminary questionnaire (enquete) examination of how effectively the facial images could be used for gender and age estimations was executed by using 300 different faces and 21 examinees. Wrinkles appeared in the face and the shape and size of the facial parts are selected to model the age and gender estimation in this research based on this enquete. Based on this preliminary consideration, an image processing algorithm for wrinkle modeling was proposed in this paper. In addition, a method for making relationships between facial images and their keywords was proposed by using the latent semantic indexing. An efficient interface for displaying the relationships among keywords and facial images has been introduced with 2D/3D-feature space defined by the indexing.

Control of home appliances using face and hand sign recognition

This article discusses face and hand sign recognition and its applications. We have already proposed a method that can recognize faces and hand signs using hierarchical discriminant analysis. Here, we implement this method on a laptop computer and develop a real-time system that can control home appliances by recognizing faces and hand signs. Our system can control various appliances in the home by remote control. Moreover, since our system can recognize the user, it can assign distinct commands to the hand signs of each user and limit the appliances and functions that users can control.

Detection of a pointing target in a real environment

For most of the existing pointing systems proposed in the past, the pointing target was limited to a specific surface in the space, usually an object on a 2-D screen. This paper presents a new solution for estimating the pointing target inside a 3-D space, such as a room. A special set of stereo cameras acquires images and depth maps from the entire environment. Using a background subtraction method, our system detects the foreground pixels, analyzes them and extracts the users face and hand regions. Considering that the pointing direction is the straight line connecting the face and the hand, the pointing target can be seen as the intersection of that line with the 3-D model of the environment, allowing the calculation and the display of the persons viewpoint.

Using an Adaptive Discriminant Feature Space in Hierarchical Face Recognition( Information Media Processing for Security)

We propose the use of an adaptive discriminant space constructed from facial images of candidate faces to optimally distinguish the faces of certain candidates. Firstly, our method to select the set of candidates uses a hierarchical combination of the four-directional features for face-direction estimation and recognition based on linear discriminant analysis. Next, the exact directions of the faces are estimated from the respective discriminant spaces for face-direction estimation. Finally, our method creates a new discriminant space from the facial images of candidates that were selected on the basis of the results of the procedure for face-direction estimation. This adaptive discriminant space is used in opimally selecting from among the candidates. Experiments showed that our method improved the accuracy in recognition by 1.6% to 99.2% by hierarchical face recognition of 105, 000 images from 150 subjects facing in 35 different directions.