Biometrics

Author(s):  
Richa Singh ◽  
Mayank Vatsa ◽  
Phalguni Gupta

The modern information age gives rise to various challenges, such as organization of society and its security. In the context of organization of society, security has become an important challenge. Because of the increased importance of security and organization, identification and authentication methods have developed into a key technology in various areas, such as entrance control in buildings, access control for automatic teller machines, or in the prominent field of criminal investigation. Identity verification techniques such as keys, cards, passwords, and PIN are widely used security applications. However, passwords or keys may often be forgotten, disclosed, changed, or stolen. Biometrics is an identity verification technique which is being used nowadays and is more reliable, compared to traditional techniques. Biometrics means “life measurement,” but here, the term is associated with the unique characteristics of an individual. Biometrics is thus defined as the “automated methods of identifying or authenticating the identity of a living person, based on physiological or behavioral characteristics.” Physiological characteristics include features such as face, fingerprint, and iris. Behavioral characteristics include signature, gait, and voice. This method of identity verification is preferred over traditional passwords and PIN-based methods for various reasons, such as (Jain, Bolle, & Pankanti, 1999; Jain, Ross, & Prabhakar, 2004): • The person to be identified is required to be physically present for the identity verification. • Identification based on biometric techniques obviates the need to remember a password or carry a token. • It cannot be misplaced or forgotten. Biometrics is essentially a multi-disciplinary area of research, which includes fields like pattern recognition image processing, computer vision, soft computing, and artificial intelligence. For example, face image is captured by a digital camera, which is preprocessed using image enhancement algorithms, and then facial information is extracted and matched. During this process, image processing techniques are used to enhance the face image and pattern recognition, and soft computing techniques are used to extract and match facial features. A biometric system can be either an identification system or a verification (authentication) system, depending on the application. Identification and verification are defined as (Jain et al., 1999, 2004; Ross, Nandakumar, & Jain, 2006): • Identification–One to Many: Identification involves determining a person’s identity by searching through the database for a match. For example, identification is performed in a watch list to find if the query image matches with any of the images in the watch list. • Verification–One to One: Verification involves determining if the identity which the person is claiming is correct or not. Examples of verification include access to an ATM, it can be obtained by matching the features of the individual with the features of the claimed identity in the database. It is not required to perform match with complete database. In this article, we present an overview of the biometric systems and different types of biometric modalities. The next section describes various components of biometric systems, and the third section briefly describes the characteristics of biometric systems. The fourth section provides an overview of different unimodal and multimodal biometric systems. In the fifth section, we have discussed different measures used to evaluate the performance of biometric systems. Finally, we discuss research issues and future directions of biometrics in the last section.

2017 ◽  
Vol 6 (3) ◽  
pp. 287-294
Author(s):  
K. Sudhakar ◽  
P. Nithyanandam

Face detection is a critical task to be resolved in a variety of applications. Since faces include various expressions it becomes a difficult task to detect the exact output. Face detection not only play a main role in personal identification but also in various fields which includes but not limited to image processing, pattern recognition, graphics and other application areas. The proposed system performs the face detection and facial components using Gabor filter. The results show accurate detection of facial components


2015 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Andi Widiyanto ◽  
Bintang Dian Mahardika

Penerapan identifikasi wajah (face recognition) telah diterapkan pada komputer, laptop atau alat-alat lain yang memang dikhususkan untuk identifikasi wajah. Perkembangan smartphone khususnya android berkembang dengan cepat. Untuk menjaga keamanan supaya hanya dapat digunakan oleh pemilik telah disediakan dengan PIN, phone code, pola geser titik sentuh layar. Aplikasi identifikasi wajah digunakan sebagai pengganti PIN atau code phone pada smartphone android dibutuhkan sebagai proteksi supaya hanya pemiliknya saja yang dapat menggunakannya. Supaya proses identifikasi wajah pemilik lebih mudah perlu dilakukan konversi dari gambar true color ke grayscale proses yang digunakan adalah pointwise. Aplikasi face recognition yang dibangun membutuhkan training wajah pemilik dengan 6 pose wajah yang disimpan, kemudian akan dibandingkan dengan identifikasi wajah saat aplikasi digunakan. Hasil pengujian menunjukkan bahwa tingkat keberhasilan antara 70% - 90%. Jarak antara wajah dan kamera serta tingkat kecerahan cahaya mempengaruhi hasil dari identifikasi wajah. Tingkat keberhasilan identifikasi wajah ditentukan oleh pengambilan image, pemrosesan image, dan perhitungan dengan PCA (eigenface).Face recognition has been implemented on a computer, laptop or other device tool which is dedicated for face identification. Developments in particular android smartphones growing rapidly. To maintain the security that can only be used by owners have been provided with a PIN, phone code, pattern shear point touch screen. Face recognition application used as a substitute for or a PIN code on the phone android smartphone needed as protection so only the owner who can use it. So that the process of identification of the owner's face needs to be done easier conversion of true color images into grayscale process used is pointwise. Face recognition application that is built requires owners face training with 6 face pose saved , then will be compared with the face identification when the application is used . The test results showed that the success rate of between 70 % - 90 %. The distance between the face and the camera and the brightness of light affect the results of face identification. The success rate is determined by identifying the face image capture, image processing, and computation with PCA eigenface.


Author(s):  
Punam Bedi ◽  
Roli Bansal ◽  
Priti Sehgal

This chapter focuses on the role of watermarking techniques in biometric systems. Biometric systems are automated systems of verifying or recognizing the identity of a living person based on a physiological or behavioral characteristic. While biometric-based techniques have inherent advantages over other authentication techniques, ensuring the security and integrity of data is a major concern. Data hiding techniques are thus used in biometric systems for securing biometric data itself. Amongst all the biometric techniques, fingerprint-based identification is the oldest and the most well established method used in numerous applications because fingerprints are unique and they remain unchanged during the human life span. However, fingerprint images should be watermarked without affecting their quality and their minutia matching ability. Moreover, if the watermark embedded in the fingerprint image is the face image of the same individual, the watermarking scheme will have two levels of security such that it will not only protect the cover fingerprint but also provides a more secure system of personal recognition and authentication at the receiver’s end. This work finds application in a number of security implementations based on multimodal biometric authentication. Computationally intelligent techniques can be employed to develop efficient watermarking algorithms in terms of watermarked image quality and distortion tolerance ability.


In most biometric-based security systems, images of the associated biometric identifiers are used as the input to that system. This chapter discusses various image processing methods and algorithms commonly used for biometric pattern recognition. Efficient and reliable processing of images is essential to achieve good performance of biometric systems. Different appearance-based methods, such as eigenimage and fisherimage, and topological feature-based methods, such as Voronoi diagram-based recognition, are discussed in the context of face, ear, and fingerprint application frameworks. Utilizing cognitive intelligence and adaptive learning methods in both physical and behavioral biometrics are some emerging new directions of biometric pattern recognition. As such, neural networks, fuzzy logic, and cognitive architectures would play a more important role in biometric domain of research. The chapter concludes with discussion of the importance of context-based recognition for behavioral biometrics.


2013 ◽  
Vol 397-400 ◽  
pp. 2148-2151
Author(s):  
Cheng Du ◽  
Biao Leng

With the development of Transportation Highway and railroad build, mining tunnel geological exploration in the road construction in the proportion of great. This paper presents a design of image processing software of Geological Engineering images for automatic analysis and processing. At present, the technology of image processing, most algorithms are based on the specific image information of specific analysis, and the face image is very complicated, different regions, and even the same construction sections in different areas of the face image may have very big difference. For the tunnel excavation face of digital image processing algorithms have little, need to start from scratch. This paper describes the use of digital image processing technology of Geological Engineering image image segmentation, found on the rock face, through the comparison of edge detection operator and Sobel Gauss - Laplasse operator methods advantages and disadvantages, a value of two images as the processing object image processing algorithm. The technology of Geological Engineering image analysis on tunnel construction period prediction plays a very important role.


2015 ◽  
pp. 1016-1040
Author(s):  
Punam Bedi ◽  
Roli Bansal ◽  
Priti Sehgal

This chapter focuses on the role of watermarking techniques in biometric systems. Biometric systems are automated systems of verifying or recognizing the identity of a living person based on a physiological or behavioral characteristic. While biometric-based techniques have inherent advantages over other authentication techniques, ensuring the security and integrity of data is a major concern. Data hiding techniques are thus used in biometric systems for securing biometric data itself. Amongst all the biometric techniques, fingerprint-based identification is the oldest and the most well established method used in numerous applications because fingerprints are unique and they remain unchanged during the human life span. However, fingerprint images should be watermarked without affecting their quality and their minutia matching ability. Moreover, if the watermark embedded in the fingerprint image is the face image of the same individual, the watermarking scheme will have two levels of security such that it will not only protect the cover fingerprint but also provides a more secure system of personal recognition and authentication at the receiver's end. This work finds application in a number of security implementations based on multimodal biometric authentication. Computationally intelligent techniques can be employed to develop efficient watermarking algorithms in terms of watermarked image quality and distortion tolerance ability.


Author(s):  
G.Y. Fan ◽  
J.M. Cowley

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.


Author(s):  
V. Jagan Naveen ◽  
K. Krishna Kishore ◽  
P. Rajesh Kumar

In the modern world, human recognition systems play an important role to   improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm.  Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.


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