This article proposes a new approach to personal authentication by exploring the features of a person’s face and voice. Microsoft’s Kinect sensor is used for facial and voice recognition. Parts of the face including the eyes, nose, and mouth, etc., are analyzed as position vectors. For voice recognition, a Kinect microphone array is adopted to record personal voices. Mel-frequency cepstrum coefficients, logarithmic power, and related values involved in the analysis of personal voice are also estimated from the voices. Neural networks,support vector machines and principal components analysis are employed and compared for personal authentication. To achieve accurate results, 20 examinees were selected for face and voice data used for training the authentication models. The experimental results show that the best accuracy is achieved when the model is trained by a support vector machine using both facial and voice features.

The personal identification from the features of personal face and voice is described in this study. The face area is detected from the picture including both the face and the complicated background by using Microsoft Kinect sensor. The personal voice is also recorded from Kinect microphone array, which is used for the personal identification. The features of the personal face are calculated from the position vectors of the face parts such as eyes, nose, mouth and so on. The mel-frequency cepstrum coefficients, the logarithmic power and their related values are calculated from the personal voice. The personal identification algorithm is defined by neural network and support vector machine. The identification accuracy of the algorithms are confirmed by the face and the voice data observed from 20 examinees. The results show that the best accuracy can be observed when both face and voice data are adopted and the algorithm is defined by the neural network.

According to progress of information society, the importance of personal authentication is growing by leaps and bounds. Because it dose not require a specific act or behavior, facial recognition is the focus of attention than other biometric identifications. The purpose of this reserch is method of extracting feature values of a human face by using Kinect sensor that is relatively inexpensive compared to the other depth cameras. And examine Kinect sensor whether the effective device for facial recognition system. In this paper,compared Neural Network, Support Vector Machine, Bayesian Network as a decision algorithm of the individual. From experimental results using still images of 10 subjects, relevance ratio of Neural Network showed the highest rate 95.6%,and considered the this method and the results.

The purpose of this research is to develop an automatic door system, which can provide intelligent opening and closing operations. The automatic door is not opened when a pedestrian just passes by the door. In order to judge the intention of the pedestrian whether he wants to pass through the door, we detect the pedestrian's skeleton data, joint orientation, and 3D coordinates by using Kinect sensor. In this paper, we analyze the behavior of pedestrians from the extracted features, and propose a method what can calculate width of pedestrian and the appropriate timing of opening operation for only whom determining to pass through the door.