Yasuhiro Mukaigawa

Gait recognition using a view transformation model in the frequency domain

Gait analyses have recently gained attention as methods of identification of individuals at a distance from a camera. However, appearance changes due to view direction changes cause difficulties for gait recognition systems. Here, we propose a method of gait recognition from various view directions using frequency-domain features and a view transformation model. We first construct a spatio-temporal silhouette volume of a walking person and then extract frequency-domain features of the volume by Fourier analysis based on gait periodicity. Next, our view transformation model is obtained with a training set of multiple persons from multiple view directions. In a recognition phase, the model transforms gallery features into the same view direction as that of an input feature, and so the features match each other. Experiments involving gait recognition from 24 view directions demonstrate the effectiveness of the proposed method.

The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication

This paper presents the largest inertial sensor-based gait database in the world, which is made open to the research community, and its application to a statistically reliable performance evaluation for gait-based personal authentication. We construct several datasets for both accelerometer and gyroscope of three inertial measurement units and a smartphone around the waist of a subject, which include at most 744 subjects (389 males and 355 females) with ages ranging from 2 to 78 years. The database has several advantages: a large number of subjects with a balanced gender ratio, variations of sensor types, sensor locations, and ground slope conditions. Therefore, we can reliably analyze the dependence of gait authentication performance on a number of factors such as gender, age group, sensor type, ground condition, and sensor location. The results with the latest existing authentication methods provide several insights for these factors. HighlightsWe present the world largest inertial sensor-based database to the community.Based on the database, females have a better recognition performance than males.People have the best recognition performance at their twenties.An accelerometer has a better recognition performance than a gyroscope.

Analysis of photometric factors based on photometric linearization.

We propose a method for analyzing photometric factors, such as diffuse reflection, specular reflection, attached shadow, and cast shadow. For analyzing real images, we utilize the photometric linearization method, which was originally proposed for image synthesis. First, we show that each pixel can be photometrically classified by a simple comparison of the pixel intensity. Our classification algorithm requires neither 3D shape information nor color information of the scene. Then, we show that the accuracy of the photometric linearization can be improved by introducing a new classification-based criterion to the linearization process. Experimental results show that photometric factors can be correctly classified without any special devices. A further experiment shows that the proposed method is effective for photometric stereo.

Photometric image-based rendering for image generation in arbitrary illumination

A Photometric Image-Based Rendering (PIER) concept is proposed that facilitates the generation of an image with an arbitrary illumination. Based on this concept, we aim to generate both diffuse and specular reflections. It is not necessary to explicitly recover 3D shape and reflection properties of the scene. In order to control appearance changes caused by modifications in the lighting conditions, we utilize a set of real images taken, in a variety of lighting conditions. Since the diffuse and specular reflection components have different characteristics, we separate these components and apply different methods to each. A photometric linearization is introduced to control diffuse reflections as well as for separating the other components. This also facilitates the treatment of attached shadows as a part of the diffuse reflection. A morphing technique is utilized to generate specular reflections. This is an effective technique for dealing with glossy objects, even when the light shape is clearly observed in the specular reflection. Experimental results show that realistic images can be successfully generated using this technique.

Similar gait action recognition using an inertial sensor

This paper tackles a challenging problem of inertial sensor-based recognition for similar gait action classes (such as walking on flat ground, up/down stairs, and up/down a slope). We solve three drawbacks of existing methods in the case of gait actions: the action signal segmentation, the sensor orientation inconsistency, and the recognition of similar action classes. First, to robustly segment the walking action under drastic changes in various factors such as speed, intensity, style, and sensor orientation of different participants, we rely on the likelihood of heel strike computed employing a scale-space technique. Second, to solve the problem of 3D sensor orientation inconsistency when matching the signals captured at different sensor orientations, we correct the sensor?s tilt before applying an orientation-compensative matching algorithm to solve the remaining angle. Third, to accurately classify similar actions, we incorporate the interclass relationship in the feature vector for recognition. In experiments, the proposed algorithms were positively validated with 460 participants (the largest number in the research field), and five similar gait action classes (namely walking on flat ground, up/down stairs, and up/down a slope) captured by three inertial sensors at different positions (center, left, and right) and orientations on the participant?s waist. HighlightsAn action recognition algorithm for similar gait actions using an inertial sensor.A robust period segmentation based on the likelihood of an heel-strike is presented.The proposed method works well against a variation of sensor orientation.Interclass relationship improves the recognition accuracy significantly.The accuracy is more than 91% for a very large database of 460 subjects.

Phase registration in a gallery improving gait authentication

In this paper, we propose a method of inertial sensor-based gait authentication by inter-period phase registration of an owners gallery. In spite of the importance for gait authentication of constructing a gallery of phase-registered gait patterns, previous implementations just relied on simple methods of period detection based on heuristic knowledge such as local peaks/valleys or local auto-correlation of the gait signals. Consequently, we propose to improve a gait gallery by incorporating a phase registration technique which globally optimizes inter-period phase consistency in an energy minimization framework. However, the previous phase registration technique suffers from a phase distortion problem due to ambiguities in the combination of a periodic signal function and a phase evolution function. We present a linear phase evolution prior to constructing an undistorted gait signal for better matching performance. Experiments using real gait signals from 32 subjects show that the proposed methods outperform the latest methods in the field.

High Dynamic Range Camera using Reflective Liquid Crystal

High dynamic range images (HDRIs) are needed for capturing scenes that include drastic lighting changes. This paper presents a method to improve the dynamic range of a camera by using a reflective liquid crystal. The system consists of a camera and a reflective liquid crystal placed in front of the camera. By controlling the attenuation rate of the liquid crystal, the scene radiance for each pixel is adaptively controlled. After the control, the original scene radiance is derived from the attenuation rate of the liquid crystal and the radiance obtained by the camera. A prototype system has been developed and tested for a scene that includes drastic lighting changes. The radiance of each pixel was independently controlled and the HDRIs were obtained by calculating the original scene radiance from these results.

Which Reference View is Effective for Gait Identification Using a View Transformation Model

Gait identification is a promising method of individual identification at a distance from a camera and identification of those who observed from various views or those who going to various directions is required in particular for actual use. In this paper, we discuss a selection of reference views for the various-view gait identification using a view transformation model (VTM). In the gait identification process, we first extract frequency-domain gait features from gait silhouette sequences, and then obtain the various-view gait features by transforming a few reference features with the VTM. We made experiments using 736 sequences from 20 subjects of 24 view directions. We evaluate the performance for each single reference and for each combination of two references. In addition, we inspect the relation between the performance and the number of references.

Shape from Single Scattering for Translucent Objects

Translucent objects strongly scatter incident light. Scattering makes the problem of estimating shape of translucent objects difficult, because reflective or transmitted light cannot be reliably extracted from the scattering. In this paper, we propose a new shape estimation method by directly utilizing scattering measurements. Although volumetric scattering is a complex phenomenon, single scattering can be relatively easily modeled because it is a simple one-bounce collision of light to a particle in a medium. Based on this observation, our method determines the shape of objects from the observed intensities of the single scattering and its attenuation. We develop a solution method that simultaneously determines scattering parameters and the shape based on energy minimization. We demonstrate the effectiveness of the proposed approach by extensive experiments using synthetic and real data.

Performance evaluation of gait recognition using the largest inertial sensor-based gait database

This paper presents the largest inertial sensor-based gait database in the world and its application to a statistically reliable performance evaluation for gait-based recognition problem. Whereas existing gait databases include at most a hundred subjects, we construct a much larger gait database for both accelerometer and gyroscope which includes 736 subjects (382 males and 354 females) with ages ranging from 2 to 78 years. Because a sufficiently large number of subjects for each gender and age group are included in this database, we can analyze the dependence of gait recognition performance on gender or age groups. The results with the latest existing recognition method provide several novel insights, such as the trade-off of gait recognition performance among age groups derived from the maturity of walking ability and physical strength. Moreover, the evaluation for the recognition performance improvement with a larger number of subjects was reliably confirmed in the experiments. As for sensor data type, acceleration is better than angular velocity for gait recognition performance. Compared to unnormalized distance (such as Euclidean distance), normalized distance (such as normalized cross correlation-based distance) works significantly better for angular velocity.