Daishi Watabe

Ear biometrics using jet space similarity

An ear biometric system that extends the ideas from elastic graph matching and principal component analysis (PCA) is introduced. For ear representation, an ldquoear graphrdquo whose vertices are labeled by the Gabor jets of body of the antihelix, superior antihelix crus, and inferior antihelix crus is introduced. A new ear detection algorithm is developed, introducing the notion of ldquojet space similarity,rdquo which denotes the similarity between Gabor jets and reconstructed jets obtained by PCA. Using the Extended Multi Modal Verification for Teleservices and Security applications (XM2VTS) database, the performance of the introduced system is found to be comparable to that of existing systems, and the detection performance is found to be higher compared with existing object detection algorithms using the distance from face space and average jets.

ICA, LDA, and Gabor Jets for Robust Ear Recognition, and Jet Space Similarity for Ear Detection

Abstract Fully automatic and robust ear recognition systems that use only 2D grayscale still images are presented. To exploit robustness against pose variation, changes in lighting, and hair occlusions, we thoroughly examined the techniques: linear discriminant analysis (LDA), independent component analysis (ICA), and Gabor jets. We obtained a 93.3% rank-one recognition rate on a dataset of 121 subjects in 4 image sets taken on various days from the public face database XM2VTS, where 47.0% of the images show hair occlusion, pose variation, and jagged images. To fully automate the recognition algorithm, we developed an ear detection algorithm that uses Gabor jets subjected to training using principal components analysis. A 1.0% equal error rate was obtained in experiments on the XM2VTS database. Our experiments provide evidence that ear biometrics has the potential for use in real-world applications for identifying individuals by their ears.

Tetranor PGDM analyses for the amyotrophic lateral sclerosis: positive and simple diagnosis and evaluation of drug effect.

Amyotrophic lateral sclerosis (ALS) is a late-onset, progressive motor neuronal degenerative disease occurring as sporadically and as a familial disorder. The patients with ALS typically become progressively paralyzed and develop respiratory failure that eventually leads to death within 3-5years. For this disease, there is no effective diagnostic method and also drug. This report describes a simple and useful diagnostic biomarker for ALS. Our findings suggest that the combination analysis of a metabolite of prostaglandin D2, 11,15-dioxo-9-hydroxy-,2,3,4,5-tetranorprostan-1,20-dioic acid (tetranor PGDM and tPGDM) with creatinine is the diagnostic approach for ALS with high accuracy. tPGDM has the potential to be an important diagnostic tool in the pre-symptomatic stages and progression evaluation of ALS, and also to be a biomarker for the evaluation of drug effect.

New Threshold Setting Method for the Extraction of Facial Areas and the Recognition of Facial Expressions

A new method for setting a threshold value on a quasi-chroma histogram to extract facial areas based on the methods used in forecasting stock market features, such as dead cross and golden cross, is proposed. In general, when a threshold value is set to isolate objects from a background, the histogram of an image is taken and examined. However, the shape of the histogram constructed from an original image is generally very jagged. Before setting the threshold, a smoothing operation is applied using a mask with the interval n. Here, the problem is in deciding the interval n and the iteration time. In general, the required interval n differs from picture to picture. Therefore, choosing the interval n is quite a delicate process. However, in this paper two feature points, dead cross and golden cross , which are generally used to decide the timing of selling or buying stocks, are adopted to set the threshold value for a quasi-chroma element image. Based on the hue element, the skin-color area is first isolated. A facial area is then extracted from the skin-color area based on a quasi-chroma histogram constructed from the skin-color area. In this step, the threshold value is set at the point where the frequency is at a minimum between the two feature points. The evaluation of the repetition time of smoothing is very important. An adequate number of iteration and interval n are also examined through experiments. In the final part of this paper, the recognition of expressions is presented

Improving the robustness of single-view-based ear recognition when rotated in depth

An algorithm is proposed that improves the robustness of ear biometric systems with the aim of developing a surveillance system based on ear biometrics. To deal with pose variations that are rotated in field depth, the Gabor jets of different poses are estimated and used as training data for a discriminant analysis-based classifier. Experimental evaluations show the effectiveness of the proposed algorithm, and the potential for improving the robustness of a single-view-based ear-biometric surveillance system.

Improving the Robustness of Single-View Ear-Based Recognition under a Rotated in Depth Perspective

An algorithm is proposed for improving the robustness of an ear biometric system with the aim of developing a surveillance-system based on ear biometrics. To deal with pose variations that are rotated in depth, the Gabor jets of different poses are estimated and used as training data for a discriminant analysis-based classifier. Experimental evaluations show the effectiveness of the proposed algorithm, and the potential for improving the robustness of a single-view-based ear surveillance-system.

Comparison of Ear Recognition Robustness of Single-View-Based Images Rotated in Depth

To improve the robustness against variation in shooting angles, we previously proposed using an asymptotic expansion of the Gabor transform of ear images to compute the Gabor features of other poses and using these estimates in multiple linear discriminant analysis to enhance feature discriminability. Extending this study, the accuracies are compared with other standard methods that can be used to compute feature vectors for other poses, as in principal component analysis and multiple regression analysis.

Real-time mobile phone dialing system based on SSVEP

Brain computer interface (BCI) systems based on the steady state visual evoked potential (SSVEP) provide higher information transfer rates and require shorter training time than BCI systems using other brain signals. It has been widely used in brain science, rehabilitation engineering, biomedical engineering and intelligent information processing. In this paper, we present a real-time mobile phone dialing system based on SSVEP, and it is more portable than other dialing system because the flashing dial interface is set on a small tablet. With this online BCI system, we can take advantage of this system based on SSVEP to identify the specific frequency on behalf of a number using canonical correlation analysis (CCA) method and dialed out successfully without using any physical movements such as finger tapping. This phone dialing system will be promising to help disable patients to improve the quality of lives.

Estimating a Shooting Angle in Ear Recognition

To improve on our earlier work on single-view-based ear biometrics, an estimation method is presented for the shooting angle of an ear image based on the summation of similarity scores over a threshold within a database of known shooting angles. Experimental results indicate that the estimation method can improve the robustness of ear recognition in varied poses.

Improving ear recognition robustness from single-view-based images rotated in depth for forensic observations

Although ear shape is used in forensic investigations, ear identification system assisting forensic experts is not well developed. One of the reasons for this is ears 3D concave shape that changes their 2D appearances when there are changes in camera angles. To compensate these changes in 2D appearance, 3D statistical modeling is necessary. For this purpose, only one model is used in our previous study. Based on a few forensic classification methods of ear shapes, we attempted to increase the number of 3D statistical ear models. Through experimental evaluation, we find a possibility to improve the robustness against change in camera angles.