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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.

We propose an eyeglass-based videophone that enables the wearer to make a video call without holding a phone (that is to say hands-free) in the mobile environment. The glasses have 4 (or 6) fish-eye cameras to widely capture the face of the wearer and the images are fused to yield 1 frontal face image. The face image is also combined with the background image captured by a rear-mounted camera; the result is a self-portrait image without holding any camera device at arm's length. Simulations confirm that 4 fish-eye cameras with 250-degree field of view (or 6 cameras with 180-degree field of view) can cover 83% of the frontal face. We fabricate a 6 camera prototype, and confirm the possibility of generating the self-portrait image. This system suits not only hands-free videophones but also other applications like visual life logging and augmented reality use.

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[%].

With the recent development of input, presentation, and processing technologies for three-dimensional (3-D) images as the background, the demand for the effective storage and management of 3-D images is expected to increase. This paper aims at the effective storage and retrieval of the 3-D images and proposes a 3-D shape indexing by the superquadrics. The features of super-quadrics are that the expression is simple and various shapes can be represented using a smaller number of parameters. To examine the applicability of the function, the technique based on superquadrics is compared with conventional 3-D modeling from the viewpoint of the power for representing the feature and shape as the technical requirement for the indexing. The purpose of the conventional modeling technique is to represent the shape from the visual sensation. However, this resulted in a problem whereby it is difficult to determine the shape from the modeled data structure. By contrast, with superquadrics, the functional parameters are easily related to the shape; thus realizing a sufficient applicability as the indexing. An image retrieval system is constructed using the superquadrics parameters as the indexes, and the usefulness of the proposed indexing technique is demonstrated.

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.

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 new electroholography system is introduced that uses a liquid crystal display, a photorefractive crystal, and a time- sharing display method. By using the photorefractive crystal as a kind of screen through which the hologram is recreated, we can realize high quality, off-axis, and computer-generated holograms. The time sharing approach enables an electronic display device to display 3D objects clearly by breaking them into parts that are displayed in sequential frames. If the fames are cycled at a high enough speed, we see these parts as a single object due to the afterimage effect. This approach relaxes the strict dynamic range requirements that would normally be placed on the electronic display device. The photorefractive crystal suppresses the flicker caused by the time-sharing display method. This paper first describes the time-sharing method. Next, a new experimental 3D-TV system with a photorefractive crystal, strontium barium niobate, is demonstrated. This system confirms the exciting future of electroholography.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper introduces a scheme whereby complex holography is realized as computer generated holograms. Complex holography is based on the complex amplitude of the wavefront using an additive representation of a real part and imaginary part. Two types of hologram are needed, but no conjugate image is produced. Conventional holography can avoid the effects of conjugate images only by using a display/recording device with very high resolution, but this solution is not available to electro-holography. The conjugate image, which is always generated from the original image simultaneously, makes the reconstructed image very noisy. Complex holography involves decomposing the complex value of each pixel into the real and imaginary parts of the pattern. Each interference pattern is displayed on a different display device, and illuminated by coherent lights. At this time, the coherent lights differ only in terms of phase, the phase difference is (pi) /2 radians. The summation of the two diffraction patterns regenerates the complex wavefront while eliminating the conjugate object light wave-front. This paper first mathematically confirms the principle of the proposed method. Next, experimental simulations in which synthesized images are clearly reconstructed are described. In addition, an experimental system is demonstrated that uses two LCD panels.