Search

Recently, research about man-machine interfaces has increased. Therefore application to facial expressions is expected from the development of the man-machine interface. An eigen-face method is popular in these research fields by using the principal component analysis (PCA). But in PCA, it is not easy to compute eigenvectors with a large matrix when considering the cost of calculation to adapt for time-varying processing. In order for PCA to become high-speed, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. A value of cos /spl theta/ is calculated using the eigenvector and the gray-scale image vector of each picture pattern. By using neural networks (NN), the value of cos /spl theta/ between true and false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smile, computer simulations are done.

In recent years, observation of a wide variety in the Earth's surface can be done by improvement of remote sensing technology. The purpose of the paper is to recognize a drift ice as thick ice, thin ice, and sea using synthetic aperture radar (SAR) images. The recognition of the drift ice is achieved by using neural networks (NN). The neural network applies two methods, a BP trained neural network and a self-organizing map. Training data are image features extracted from SAR images. There are three methods for extracting the features: Fourier transform, high-order autocorrelation function (HACF), and image features based on a run length method. We carry out a comparative experiment, and demonstrate their effectiveness by means of computer simulation.

The scene illumination that is present when an image is captured plays a major role in the appearance of the colors of the objects in that image. This is because color is not a physical property of objects and therefore, it is not constant under most conditions, for example, changing scene illumination. We target skin color as the object of interest in an image. We used neural networks to learn and identify which of four different types of scene illuminations were present in a given scene. A real coded genetic algorithm was also used to shrink the size of the neural network input data, and to identify the areas of a scene that contain the most information about, and best represent the scene illuminant Once a scene illuminant can be identified, it can go a long way in helping to correct and normalize skin color in systems' that are sensitive to skin color changes, for example face detection and recognition.

We propose a new method to examine whether or not human faces are included in color images by using a lip detection neural network (LDNN) and a skin distinction neural network (SDNN). In conventional methods, if there exists the same color as the skin color in scenes, the domain which is accepted as not only the skin color but any other color can be searched. However, first, the lips are detected by LDNN in the proposed method. Next, SDNN is utilized to distinguish skin color from the other colors. The proposed method can obtain relatively high recognition accuracy, since it has the double recognition structure of LDNN and SDNN. Finally, in order to demonstrate the effectiveness of the proposed scheme, computer simulations were performed. First, 100 lip color, 100 skin color and 100 background pictures, which are changed into 10/spl times/10 pixels, are prepared for training. The validity was verified by testing images containing several faces.

Recently in the world, many researches of individual recognition method using biometrics are widely done. Especially, individual recognition using faces are used because of needless for physical contact. When we specify with an individual, the information from a face is the most reliable. In this paper, we propose the new method of individual recognition using Simple PCA and NN. Individual recognition system needs to have not only ability to recognize the registrant correctly but also ability to reject the other person certainly. therefore, our objective is to reject certainly the other person. Furthermore, we analyzed and examined about the individual feature in a face using GA. Moreover, in order to show the effectiveness of the proposed method, we show computer simulations by using real image. From these results, we show the effectiveness.