Authentication methods with password, PIN, face recognition, and fingerprint identification have widely been used, however these methods have problems of difficulty in one-handed operation, vulnerability to shoulder hacking, and illegal access using fingerprint with super glue or facial portrait. Prom viewpoint of usability and safety, strong and uncomplicated method is required. We propose a user authentication method based on grip gestures using pressure sensors mounted on the lateral and back of a mobile phone. Grip gesture is an operation of grasping a mobile phone, which is assumed to be done instead of conventional unlock procedure. Grip gesture can be performed with one hand. Moreover, it is hard to imitate grip gestures since finger movements and grip force during a grip gesture are hardly seen by the others. We experimentally investigated the feature values of grip force and evaluated our proposed method from viewpoint of error rate.

We propose an algorithm for authenticating the user of a mobile phone from the outputs of pressure sensors during key-operations such as button-pushes. While not intended to replace password identification, it does help in providing the service which is suitable for a user without any his/her specific action. For example, during user's entering key strokes. if a service cloud can recognize user-authentication by analyzing key strokes, then, it can find the optimal services based on the user preference. Our algorithm is based on a statistic probabilistic model based approach; it calculates the probability distribution of the temporal differential values of pressure by Kalman filtering. The captured sensory data is compared to predicted sensory data based on the probability distribution to judge whether the person making the key-operation is the registered owner or not. We implement the proposed system and subject it to feasibility experiments with 10 subjects; its user-authentication accuracy is quite good with a FAR-FRR error rate of only 10[%].

A framework and method are proposed to extract local features of a certain kind of naturally occurring, non-rigid motion pattern, referred to as temporal texture. To catch both the spatial and temporal features of this complex pattern, we focus on the surfaces of motion trajectories in spatiotemporal space derived from multiple frames of an image sequence, and represent the surfaces as a set of tangent planes of the surfaces. From the distribution of the tangent planes in local regions in time and space, spatial and temporal texture features are computed The features considered here include spatial arrangement of dominant contours, uniformity of velocity components, and trajectory run length. Experimental results show that the newly defined features have the capability of quantifying the features of complex motion patterns such as weather radar images.

A new framework and method, based on image motion trajectories in spatiotemporal space (x-y-t space), are proposed to estimate image velocity from an image sequence. We focus on the surfaces of the trajectories in the x-y-t space formed by the edges and contours of moving objects and obtain image velocity from the orientation of the intersection line formed by tangent planes on the trajectories. The proposed method includes two Hough transforms to detect the most dominant orientation in all possible intersection lines and reliably produces the dominant translational image velocity semi-locally. Also, the confidence measure of estimates is defined to decide the optimal size of patch that suppresses the aperture problem. Experimental results from several synthetic and real image sequences are presented to verify the effectiveness of the method and to confirm its robustness against noise and occlusion.

A method which produces a 3-D structural description from contour data or sparse range data by using superquadrics is proposed. The method consists of primal segmentation using superquadrics and convex parts merger using Akaike's information criterion (AIC) as the criterion. The primal segmentation operation produces many convex parts by expanding superquadrics within the object. The AIC criterion enables keeping the number of parts reasonable because it determines how many parts form the object. An AIC for merging superquadrics is derived. The proposed method is tested successfully by transforming contour data and 3-D sparse range data of human beings into parts descriptions. The tests indicate that the proposed method can be used for 3-D object recognition as well as for data capture for computer graphics applications.>

A multiscale 3-D structure recovery method is proposed. It divides the scene space into voxels and votes for them using the sight lines of the camera from coarse to fine. The mechanism behind the influence of the image quantization error and the position error of the camera's optical center is analyzed. Both errors can be diminished by /spl Delta//sup 2/G filtering the voted voxel space. A multiscale 3-D structure of objects is successfully generated from a real image sequence of six frames using this method.>