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Purpose – User authentication is generally used to protect personal information such as phone numbers, photos and account information stored in a mobile device by limiting the user to a specific person, e.g. the owner of the device. Authentication methods with password, PIN, face recognition and fingerprint identification have been widely used; however, these methods have problems of difficulty in one-handed operation, vulnerability to shoulder hacking and illegal access using fingerprint with either super glue or facial portrait. From viewpoints of usability and safety, strong and uncomplicated method is required. Design/methodology/approach – In this paper, a user authentication method is proposed based on grip gestures using pressure sensors mounted on the lateral and back sides 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, as finger movements and grip force during a grip gesture are hardly seen by the others. Findings – The feature values of grip force are experimentally investigated and the proposed method from viewpoint of error rate is evaluated. From the result, this method achieved 0.02 of equal error rate, which is equivalent to face recognition. Originality/value – Many researches using pressure sensors to recognize grip pattern have been proposed thus far; however, the conventional works just recognize grip patterns and do not identify users, or need long pressure data to finish confident authentication. This proposed method authenticates users with a short grip gesture.

We propose two statistical probability approaches to extract personal feature from the user's grip force data. One approach is based on grip force changes predicted by the Kalman filter, the other is based on distributions of grip force changes by Jensen-Shannon(JS) divergence. Personal feature is the customary behavior that repeatedly appears without the user being aware of it. The personal feature is used for not only user-authentication, but also user-special commands. We mount pressure array sensors on a mobile phone and show that our proposals can extract personal feature from the user's grip force data with 10[%] error in FAR-FRR by the Kalman filter approach and the accuracy of 100[%] by the JS divergence approach.

A hierarchical matching is the most efficient way to recognize a 3-D object. In most conventional methods of 3-D object recognition, a hierarchical structure is formed after the detailed 3D structure of an object has been extracted by dividing it coarsely. This approach is very inefficient, since it is difficult to obtain a detailed 3D structure initially in addition to a large amount of computation time. This paper proposes a method which extracts a multiscale 3D structure directly from coarse to fine. In this method, a space containing an object is divided into coarse voxels, and a “3D voting” is applied. This voting adds a certain value to a voxel through which a back-projected line connects the center of the camera lens and a feature point on an image. A voxel having a high value is regarded as a region containing a 3D feature point, and this is extracted. Extraction of a hierarchical structure is carried out by repeating the dividing process from coarse voxels to fine voxels so that the operation becomes efficient. This paper also analyzes the effects of the deviation of a feature point in an image and the deviation of the camera center on the 3D voting. The paper shows also that an application of a ▽2G filter to the voxel space (after 3D voting) is effective in suppressing the errors due to the deviations. The method has been confirmed successfully by processing six complex test images.

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 novel framework called memory-based forecasting is proposed to forecast complex and time-varying natural patterns. In this framework, past patterns similar to the present pattern are retrieved, and the forecast pattern is produced by using the patterns that follow the retrieved sequences. We represent the dynamic features of a sequence by using the spatial distribution of the patterns, velocity field, and temporal texture features; the sequences are transformed into paths in eigenspaces. The relationship between retrieval error and prediction error is modeled to define a similarity measure for retrieval. Forecast images are constructed from a future point in the eigenspace which is estimated by a nonlinear prediction scheme. Several experiments using weather radar images confirm the effectiveness of our method especially for drastically changing patterns.