Biometrics and Kansei Engineering

This chapter will show and discuss some facts, concepts, and approaches concerning the principles of biometrics from research point of view. In particular, the chapter introduces a deep study on the meaning of preprocessing principles as for image analysis and processing in biometrics. The effects and consequences of noisy or unfiltered biometric images are discussed in some detail with examples on fingerprints. Some new biometric features like the human nail lunula are also presented for future eventual consideration. The biometrics error rates with their graphical representation in biometric systems are illustrated in some detail. Also discussed is how the biometric features are not stable and permanent with age. Example of face changes is given for discussion.

This chapter addresses the problem of face recognition from images with lighting problems such as shadows or brightness level. Authors describe face recognition processing, including major components such as face detection, tracking, alignment, and feature extraction. Technical challenges of building a face recognition system are pointed out. The chapter emphasizes the importance of subspace analysis and learning, providing not only an understanding of the challenges therein but also the most successful solutions developed to date. In the following sections, authors present brief history of face recognition systems, show problems that affect results of these systems, and present their own approach based on finding fiducial points in face image and their further use for face recognition.

The work deals with the iris pattern recognition as one of the most popular automated biometric ways of individual identification. It is based on the acquired eye images in which we localize the region of interest – the iris. This extremely data-rich biometric identifier is stable throughout human life and well protected as internal part of the eye. Moreover, it is genetic independent, so that we can use it to identify or verify people among huge population. This chapter will present the human vision nature focusing on defects and diseases that change the surface information of the iris. Also will be shown the main stream and the historical background of mathematical research resulting in a new algorithm for automatic iris feature extraction. A special attention is paid to the method developed to detect the iris rotation for accurate success rate under different destructive problems and environmental conditions. The obtained results after using the new mathematical model have proved the algorithm high success rate in iris pattern recognition.

This chapter introduces an interesting laboratory method for human identification by finger vein patterns. For image capture, a simple webcam is used. The electronic circuit (the hardware part) is built from simple components with a low cost. The whole device is of small size and can be used in the laboratory for student laboratory application. The implemented algorithm and hence the testing software have shown very high success rate. For finger vein image denoising and binarization, a method worked out in the authors’ team is applied. For easier feature extraction and data reduction, the authors make the necessary thinning by universal algorithms like Zhang or K3M. A big challenge is still to get efficient connection between image processing, securing data, and database exploration.

The focus of this chapter is on security of touch screen devices. The emphasis is placed on smartphones – such as Apple iPhone and Android phones – and tablets – such as Apple iPad. The chapter starts with the description how the touch screen devices are winning a significant share in the market. The current state of the security methods used on these devices is discussed. Deficiencies of prevailing approaches are pointed out, and the need for new authentication mechanisms is reasoned. The hardware available in modern touch screen devices is characterized, and the sensors providing biometric data are described.

In the last part of this chapter, some of the new security means using biometric features and potential new directions are discussed.

Biometric identification using modalities based on the human body is being actively studied. For physical biometrics, such as fingerprints, veins, irises, and faces, which have been widely used for identification, spoofing is recognized as a serious vulnerability. In an attempt to prevent spoofing, here, we have proposed three new biometrics approaches based on various features of the eyes, namely, the behavioral characteristics of eye movement, three-dimensional shape of the iris surface, and a combination of measuring the contour of the upper eyelid during blinking, respectively. All three proposed methods have high accuracy of identification and are resistant to spoofing. In this chapter, the respective characteristics, experimental devices, algorithms of image processing, evaluative experiments, and summary of each proposed method are described. In the final section, the characteristics and accuracy of each method are compared and discussed.

This chapter presents a new local texture descriptor, local quaternary patterns (LQP), and its extension, feature local quaternary patterns (FLQP). The LQP, which encodes four relationships of local texture, includes more information of local texture than the local binary patterns (LBP) and local ternary patterns (LTP). The FLQP which encodes both local and feature information is expected to perform better than the LQP for texture description and pattern recognition. To reduce the size of feature dimensions and histograms of both LQP and FLQP, a new coding schema is proposed to split the LQP and FLQP into two binary codes: the upper and lower binary codes. As a result, the total possible values of split LQP and FLQP are reduced to 512. The feasibility of the proposed LQP and FLQP methods is demonstrated on an eye detection problem. Experimental results using the BioID database show that both the FLQP and the LQP methods archive better eye detection performance than the feature LTP, the LTP, the feature LBP, and the LBP methods. Specifically, the FLQP method achieves by far the highest eye detection rate among all the competing methods. In addition, the FLQP method has the lowest average relative distance error γ, while the average γ of the LQP, the feature LTP, the LTP, the feature LBP, and the LBP methods are 5.8%, 7.5%, 9.7%, 6.9%, and 125.6% higher than the average γ of the FLQP method, respectively.

In this book chapter, a novel biometric system from the normalisation level up to the verification level is developed, tested and verified against other multimodal and unimodal systems. The main advantage of a new architecture is in flexibility of combining various features from multimodal biometrics in a new way, suitable for neural-network learner. The system utilises associative memories and pattern matchers as learners of biometric data, but the main advantage of a new architecture is increased resistance to noise and ability of system to compensate for an absence of some biometric traits. Detailed experimental analysis of pros and cons of such system is also provided.

Aiming at improving the quality of life (QOL) of people in everyday living, Kansei Engineering (KE) has been proposed. Since then, while industrial sectors in Japan have applied KE, its methodology has not made much marked progress. The fact that the foundation of KE differs from any of modern technologies seems to bring some difficulties to its application. In order to reduce such difficulties, we begin with the fundamental definition of Kansei and a brief introduction to the characteristic elements of technologies in KE.

This chapter explains human being and Kansei engineering which is the basis of postmodern technology. Modern technology has taken an important role after the industrial revolution. The society has been developed according to the modern technology, but it comes to a deadlock now. Therefore, our main concern is how to evade the deadlock. We proposed postmodern technology named Kansei engineering which was based on Kansei. Kansei is an integrated human ability which includes emotion, feeling, recognition, modeling, and expression. The new technology is characterized by an interactive method and a network structure. The beauty of modern technology as a single logic method of deduction exists in simplicity and power of exploitation, while the beauty of Kansei technology exists in complexity and tolerance in heterogeneity. We apply Kansei technology to industry, and then we can escape from various kinds of crises.

Kansei engineering has proposed a novel system of production and services which evokes the user’s mind/sense of value as embedded in the products or services. In this system, interactions between the products and the human mind are the core issue in developing the products. While its applications in industries have been stimulated, methodological development has not been marked yet. Aiming at promoting the method capable of developing basic ideas of Kansei engineering, we discuss the two fundamental topics in it. The first topic is concerned with the notion of Kansei and its varieties. We examine Kansei in relation to philosophy centered on aesthetics established in the eighteenth century. The second issue is concerned with a possible relation of Kansei to thinking within the human mind, that is, the problem of how it forms connection into thoughts. We present two kinds of aesthetic thoughts as concrete examples. Finally, we discuss the problem of quality evaluation of products, that is, how to evaluate products from Kansei point of view.

This chapter concentrates on topics of “Kansei” measurement to create the new criterion through measuring human’s action and psychological and physiological responses. “Kansei” is the ability to receive the variety of information coming from another party, understand, and then apply this information to one’s own thoughts. Kansei is absolutely indispensable to interactive dialogue. For interactive co-creation, consumers and manufacturers creating products through mutual and consensual dialogue and discussion are completely necessary. Nevertheless, it is extremely difficult for us humans to communicate our thoughts and consider others’ desires. We must create a new criterion for understanding each other via physiological response. The availability of diverse methods of interaction promotes interpersonal communication and deepens mutual understanding.

This chapter provides an application of some notions in information theory to Kansei engineering toward a mathematical methodology for the analysis in Kansei engineering field. First, we propose a new definition of correlation using the mutual information in information theory. Second, we present a relation between Bayes’ updating using the notion of binary channel and sequential probability ratio test (SPRT). Third, we illustrate that, for structural change detection of time series data, the SPRT is a very useful method that meets our intuition or Kansei very well. Moreover, we show the effectiveness of SPRT, comparing with Chow test that is well known as a standard method for the change detection.

What are differences and similarities between Kansei engineering and service engineering? Control theory and measurement technology provide an analogous perspective. However, tools for service engineering are also useful for implementation of Kansei-type agents. For Kansei engineering, an individual sense of value is an important factor. Therefore, relations of a target system are analyzed by the covariance structure analytical method and expressed with ontology to construct a general structure of the system. Then individual relations are examined using a Bayesian network to treat them precisely. We present practical applications as examples.