Hironori Takimoto

Apparent age estimation system based on age perception

The age is one of important information in our living. If the age estimation that uses face image by computer becomes possible, it is thought that the age estimation assumes an important role in various scenes. In this paper, we propose an age estimation every age by using the supervised SOM. Furthermore, the important features for the age estimation are selected by the GA.

Classification of hand postures based on 3D vision model for human-robot interaction

In this paper, a method for hand posture recognition, which is robust for hand posture changing in an actual environment, is proposed. Conventionally, a data glove device and a 3D scanner have been used for the feature extraction of hand shape. However, the performance of each approach is affected by hand posture changing. Therefore, this paper proposes the posture fluctuation model for efficient hand posture recognition, based on 3D hand shape and color feature obtained from a stereo camera. A large set of dictionary for posture recognition is built by various leaned hand images which were auto-created from one scanned hand image, based on plural proposed models. In order to show the effectiveness of proposed method, performance and processing times for posture recognition are compared to conventional method. In addition, we perform the evaluation experiment by using the Japanese sign language.

A robust gesture recognition based on depth data

In this paper, we propose a novel method for gesture recognition using depth data captured by Microsoft Kinect sensor. Conventionally, the features which have been used for gesture recognition are divided into two parts, hand shape and arm movement. In conventional methods, only two-dimensional hand features are used because humans hand consists of the multiple joint structure. Furthermore, conventional arm movement feature are influenced by environmental changing, such as individual differences in body size, camera position and so on. Therefore, to assist in the recognition, a method of feature extraction is proposed, which involves the hand shape with 3D feature and the arm movement with angle between joints of body. In order to show effectiveness of the proposed method, performance for gesture recognition is compared with conventional methods using Japanese language.

A human tracking mobile-robot with face detection

In this paper, we propose the face detection method for tracking a human by a mobile-robot. We obtain images from a web camera, and detect faces by focusing on skin colors and eyes as facial features. If we detect faces from images, we trace the detected human, take a picture of him/her, and print it automatically by using the mobile-robot. In order to show the effectiveness of the proposed method, we show the experimental results. Firstly, in the face detection, we show the face detection accuracy. Then, in the human tracking with mobile-robot by using face detection, we show the tracking performance.

A Design of Gender and Age Estimation System Based on Facial Knowledge

The purpose of this paper is to propose a method of gender and age estimation which is robust for environmental changing. We propose a feature-point detection method which is the advanced retinal sampling method (ARSM), and a feature extraction method. As features for the gender and age estimation, facial shape, skin texture, hue and Gabor-feature are used. In order to show the effectiveness of proposed method, not only real-age database of facial image but also appearance-age database is employed. We also analyze the facial features characteristic to each age category and gender, and examine the difference feature of between the real-age and appearance-age in a facial area. Moreover, we examined the left-right symmetric property of the face concerning gender and age estimation by the proposed method.

Face detection and emotional extraction system using double structure neural network

In this paper, 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 is 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 lip is detected by LDNN in the proposed method. Next, SDNN is utilized to distinguish the skin color from the others. 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 pixel, are prepared for training. The validity was verified by testing images containing several faces.

Appearance-age feature extraction from facial image based on age perception

An age is relatively important information in the various features which are shown human from face. It is possible to apply it to an amusement and an aesthetic surgery by analyzing the appearance-age feature which is used for age perception in a facial region. In this paper, we analyze facial feature of appearance-age based on age perception. In order to construct the appearance-age database, age estimation experiment is performed to the HOIP facial database by 15 subjects. The appearance-age feature used potentially when human performs age estimation is decided by using the genetic algorithms and the neural network. By using the proposed method, facial features that influenced on the appearance-age in each gender was confirmed. Moreover, it is suggested that that appearance-age feature which is used for age estimation is different in each generation when human performs age estimation.

A Robust Gesture Recognition Using Depth Data

In this paper, we propose a novel gesture recognition system using depth data captured by Kinect sensor. Conventionally, the features which have been used for hand gesture recognition are divided into two parts, hand shape features and arm movement features. However, these traditional features are not robust for environmental changing such as individual differences in body size, camera position and so on. In this paper, we propose a novel hand gesture recognition system using depth data, which is robust for environmental changing. Our approach involves an extraction of hand shape features based on gradient value instead of conventional 2D shape features, and arm movement features based on angles between each joints. In order to show the effectiveness of the proposed method, a performance is evaluated comparing with the conventional method by using Japanese sign language.

Age and gender estimation by using facial image

In this paper, we propose age and gender estimation system by vairous features. Age and gender has a lot of characteristics. These characteristics are one of the difficult cognitive process in human interaction. If we can extract the important feature of this cognitive process, it is considered that the age and gender estimation by the machine becomes possible. Therefore, we propose a method of age and gender feature extraction and estimation using the face image. In this paper, the age of the face means apparent-age that is based on the human perception of age. Moreover, persons aging and gender difference appear in the faces. For example, the pigmented spot, the wrinkle, sagging skin, shape, color of skin, and so on. Thus, we extract these several features for age and gender estimation. Furthermore, we estimate a continuous age and gender using a neural network (NN).

An apparent age estimation system using the evolutionary algorighm

The age is one of important information in our living. If the age estimation that uses face image by computer becomes possible, it is thought that the age estimation assumes an important role in various scenes. In this paper, we propose an age estimation by using the supervised SOM. Further, the important features for the age estimation are selected by the GA.