Biometric the first functional system of fingerprint identification and

Biometric Sensors
Yash Patela, Shweta Mohana, Prof. Sejal Bhavsara

aGandhinagar Institute of Technology, Gandhinagar, 382721, India 

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The face is a image of the mind with the eyes as its interpreter. Face detection has been one of the most important topics in the biometric sensors. Face recognition has received significant attention to nowadays. As one of the most successful automated biometric of image understanding and analysis. It is not a simple multidimensional structure and  needs a better computing technique for biometric recognition sensors.Wide range of commercial and law enforcement application and availability of feasible technologies such as  passport, smart cards, PIN, banking, network security, credit cards and access control are the reasons for this trend. Even though current machine recognition system and machine recognition devices have reached a certain level of stability. Their success is limited by the condition imposed by many real application and devices. Hence current system are still far away from the capability of the human perception system. This paper provides survey on biometric recognition sensors research. The motive of this paper is to provide some insights into the study of biometric recognition system. 

Keywords: biometric system; biometric sensors; biometric traits; biometric; biometric survey; biometrics application; biometric recognition sensors 

1. Introduction 

The biometric is the study of physical or behavioural characteristics used for the identification of a person. These characteristics of a person include the features like fingerprints, face, hand geometry, handwriting, voice, retinal, vein and iris biometric features. Biometrics is often remarks as the highest level of security or access control with accuracy then 95%.

During the 1880s, Alphonse Bertillon, a European police officer, was evidently the first person to create an identification system which is based on the measurement various physical characteristics. This technique is called anthropometry. Fingerprint is the most commonly used Biometric Identifier. Sir Francis Galton first began observing fingerprints as a means of identification. Juan Vucetich, an Argentine police official developed further on Bertillon system and Galton pattern types and devised the first functional system of fingerprint identification and used it in a murder investigation in 1892. Nowadays, Biometrics is used extensively where unique identification of an individual is required. As of December 2017, the Unique Identification Authority of India (UIDAI) issued over 1.19 billion Aadhar cards which involves biometric information like fingerprint and iris images. This is the world’s largest Biometric database. 

In sprite of its accuracy and high level security, Biometrics is widely used for unique identification rather than physical security or access control. This is because of the challenges faced in this field. Providing solution for these challenges may result in the widespread adoption of Biometrics.

All the authentication methods can be summarised into three types-

Something you know (e.g. a Password)
Something you have (e.g. a Smart card)
Something you are (e.g. a Biometric)

In the first type, a person is expected to remember something which he might forget. In the Second type, a person is expected to possess something. It is possible that he might lose this possession. But the third type of authentication is physiological characteristics of a person. Some characteristics are unique to every person and are difficult to spoof, which forms the basis for Biometrics and also make it more effective. It is not easy to guess or steal the data unlike password or tokens.
2. Background 
A biometric is a physiological or behavioural characteristic of a human being that can distinguish one person from another and that theoretically can be used for identification or verification of identity. There are two types of biometric applications namely Physiological and Behavioural. The suitable biometric can be selected depending upon the application in various computer based security systems. The physical characteristics of a person like fingerprints, hand geometry, face, voice and iris are known as biometric. Each biometric traits has its strengths and weaknesses. The important feature of the various biometric are discussed briefly in this section.     

There are many kinds of Biometric technologies and each Biometric has its own advantages and limitations. The choice of using a specific Biometric depends on the application. Any single Biometric cannot meet all the requirements of all the applications. Following are some of the commonly used Biometrics: 









Fig. 1. Types of Biometric Application

    Fingerprint have long been used by human for personal identification because of its high accuracy, consistency over time and uniqueness. Fingerprint identification is popular because of the inherent ease in acquisition, the numerous sources (ten fingers) available for collection and their established use and collection by law enforcement and immigration. Its also inexpensive and easy to use. This is the most widely used Biometric recognition of all. Multiple fingerprints of an individual provides more information and security.

A fingerprint usually appears as a series of dark lines that represent the high, peaking portion of the friction ridge skin, while the valleys between these ridges appears as white space and are the low, shallow portion of the fiction ridge skin. Fingerprint identification is based primarily on the minutiae or the location and direction of the ridge ending and splits along a ridge path.

Facial image are the most common Biometric characteristic. Humans are personally recognised through their face. This is a nonintrusive method and is suitable for covert recognition applications. There are ample of applications such as face identification in a cluttered background such as airport, subway, railway, bus stands etc. 

Facial recognition system should be able to automatically detect a face in an image, extract its features and then recognise it from a general viewpoint which is a rather difficult task. Another problem is the fact that the face is a changeable social organ displaying a variety of expressions. These systems also have difficulties in recognising a face from images captured from two different angles and under different ambient illumination conditions.Line drawings should be good quality scans or true electronic output. Low-quality scans are not acceptable. Figures must be embedded into the text and not supplied separately. Lettering and symbols should be clearly defined either in the caption or in a legend provided as part of the figure. 
It is the annular region of the eye surrounded by the pupil and the sclera on either side. During the first two years of life, the visual texture of the iris is formed. A fully developed iris carries very unique information useful for personal recognition. But the system might be expensive and usage might be complex.

Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual’s eyes, whose complex random patterns are unique, stable, and can be seen from some distance. Iris recognition uses video camera technology with subtle near infrared illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally. The iris image consists of the coloured tissue surrounding the pupil .The iris recognition systems are known as real time, high confidence recognition of person identification. These systems are used in many applications like Aadhar, passports, activation security, and controlling access to restricted areas at airports, database access and computer login, access to building and homes, border crossings and other government programme.

The iris recognition systems behave following features:

Perform 1: n identification with no limitation on numbers.
The most robust biometric technology available in the market today never had a false acceptance.
Biometric templates once captured do not need to be enrolled again, iris stable throughout a human life.

The retinal vasculature is claimed to be the most secure Biometric since it’s not easy to change or replicate. The image acquisition requires a person to peep into an eye-piece and focus on a specific spot so that the retinal vasculature can be imaged. No single technique can outperform all the others in all operational environments. In other words, there is no optimal Biometric characteristic.

The retina is a thin layer of cells at the back of the eyeball of vertebrates. It is the part of the eye which converts light into nervous signals. It is lined with special photoreceptors which translate light into signals to the brain. The main features of a funds retinal image were defined as the optic disc, fovea, and blood vessels. Every eye has its own totally unique pattern of blood vessels. The unique structure of the blood vessels in the retina has been used for biometric identification. Since it is protected in an eye itself, and since it is not easy to change or replicate the retinal vasculature, this is one of the most secure biometric. 

Palm print recognition is one of the popular methods which have been investigated over several years due to its number of advantages such as stable line features, low-resolution imaging, low-cost capturing device, and user-friendly. Palm print recognition uses the person’s palm as a bio-metric for identifying or verifying person’s identity. Palm print patterns are a very reliable biometric and require minimum cooperation from the user for extraction. Palm print is distinctive, easily captured by low resolution devices as well as contains additional features such as principal lines, wrinkles and ridges. Therefore it is suitable for everyone and it does not require any personal information of the user. Palm normally contains three flexion creases (principal lines), secondary creases (wrinkles) and ridges. The three major flexions are genetically dependent; most of other creases are not. Even identical twins have different palm prints. These non-genetically deterministic and complex patterns are very useful in personal identification. Palm is the inner surface of the hand between the wrist and fingers. Palm area contains large number of features such as principle lines, wrinkles, minutiae, datum point features and texture images. Most of the system uses the low resolution image. 

  Palm print recognition has considerable potential as a personal identification technique as it shares most of the discriminative features with fingerprints and in addition possesses a much larger skin area and other discriminative features such as principal lines, ridges and wrinkles which are very useful in biometric security. Coding based techniques have proven to be efficient in terms of memory requirement and matching speed. Fusion technique is recent area in which researchers used to fuse features like appearance-based, line and texture features from palm-prints, which has led to an increase in accuracy. Recent work involves use of multi-scale, multi-resolution based techniques like wavelets and contour lets are for efficient implementation of palm print recognition.

The use of various biometric traits such as fingerprint, face, iris, ear, palm print, hand geometry and voice has been well studied. It is reported that the skin pattern on the finger-knuckle is highly rich in texture due to skin folds and creases, and hence, can be considered as a biometric identifier. Each finger has three joints .There are three bones in each finger called the proximal phalanx, the middle phalanx and the distal phalanx. The first joint is where the finger joins the hand called the proximal phalanx. The second joint is the proximal interphalangeal joint, or PIP joint. The last joint of the finger is called the distal interphalangeal joint, or DIP. Finger knuckle is the back surface of finger, it is also known as dorsum of the hand. The inherent skin patterns of the outer surface around the phalangeal joint of one’s finger, has high capability to discriminate different individuals. Such image pattern of finger knuckle is unique and can be obtaining online, offline for authentication. Extraction of features of knuckle for identification is totally depends upon the user. 

Voice recognition, speech recognition and Natural Language processing are a group technologies connected and related to take human voice and converting it into words, verify, and understand commands. The speech recognition is most important research area in the today’s world. There are various speech recognition approaches; among those are the acoustics phonetic pattern comparisons and automatic speech recognition approach. The voice recognition biometric systems are used for access control, banking, government offices and entertainment applications, smart cards, PIN and other security purposes. 

The features of person voice are based on the vocal tracts, mouth, nasal activities and lips movement that are used synthesis of sound. These physical characteristics of human speech are invariant for individuals. The behavioural part of the speech of person changes over time due to age, medical conditions, and emotional state. The speaker dependent voice recognition systems are text dependent; and the speaker independent systems are what he or she speaks. The speaker dependent voice recognition system is more difficult to design but provides more protection.

Keystroke dynamics refers to the process of measuring and assessing human’s typing rhythm on digital devices. Such device, to name a few, usually refers to a computer keyboard, mobile phone, or touch screen panel. A form of digital footprint is created upon human interaction with these devices. These signatures are believed to be rich in cognitive qualities, which is fairly unique to each individual and holds huge potential as personal identifier.

The emergence of keystroke dynamics biometrics was dated back in the late 19th century, where telegraph revolution was at its peak. It was the major long distance communication instrument in that era. Telegraph operators could seamlessly distinguish each other by merely listening to the tapping rhythm of dots and dashes. While telegraph key served as an input device in those days, likewise, computer keyboard, mobile keypad, and touch screen are common input devices in the 21st century. Furthermore, it has been noted that keystroke pattern has the same neurophysiologic factors that make hand written signature unique, where humans have relied on to verify identity of an individual for many centuries. In fact, keystroke pattern is capable of providing even more unique feature for authentication, which includes key press duration and latencies, typing rate, and typing pressure.

Biometric authentication using signatures can be realised with no additional sensor except a pen and a  piece  of paper.  Nakanishi  et al. have  shown  that  every  human being  has limited biometrics and  if  the biometric data are leaked out  or  accessed inadvertently, and the identity of the person whose biometrics they belong to is disclosed, they can never be used for authentication again. So, to deal with this problem, cancellable biometric techniques have been introduced.  Among  various biometric modalities, only the signature is considered cancellable from a viewpoint of spoofing. Even if a  signature shape is known by others, it is possible to cope with the problem by changing the shape. Among all of the biometric authentication systems that have been proposed and implemented, automatic handwritten signatures are  considered as the most legally and socially accepted attributes for personal identification. The most challenging aspect in the automation of signature-based authentication is the need for obtaining high accuracy results in order to avoid false authorization or rejections. 

Handwritten signature authentication is based on systems for signature verification and  signature identification. Whether the given signature belongs to a particular person or not is decided through a signature  identification system, whereas the signature verification system decides if a  given signature belongs to a claimed person or  not. Signature-based authentication can be either static or dynamic. In the static mode (referred to as off-line), only the digital image of the signature is available. In the dynamic mode, also called “on-line”, signatures are acquired by means of  a graphic tablet or a pen-sensitive computer display. 
3. Conclusion
This paper provides a detailed survey on recognition of different biometric traits like face, finger, palm, voice, knuckle, retinal, signature, keystroke and iris. Article stated with the introduction of different physiological and behavioural biometrics traits used in security different commercial and security applications.
4. Acknowledgement
We have taken efforts in this project. However, it would not have been possible without the kind support and help of many individuals and organisations. We would like to extend our sincere thanks to all of them. 

We are highly indebted to Prof Sejal Bhavsar for their guidance and constant supervision as well as for providing necessary information regarding the project. We take this opportunity to thank all my friends and colleagues who started us out on the topic and provided extremely useful review feedback and for their all-time support and help in each and every aspect of the course of our project preparation. We are grateful to our college Gandhinagar Institute of Technology, Gandhinagar for providing us all required resources and good working environment. We would like to express my gratitude towards Head of Department, Prof Archana Singh and Director, Dr N M Bhatt for their kind co-operation and encouragement which help us in this project.
5. Reference 
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2 Libor Masek, the University of Western Australia, “Recognition of Human Iris Patterns for Biometric Identification”, 2003.

3 Ed German, “The History of Fingerprints”, (accessed October 14, 2014).

4 Unique Identification Authority of India (UIDAI) issues 56 crore Aadhaar Numbers”, Press Information Bureau, Government of India, Planning Commission, entry posted January 16, 2014, 

5 I. BioPassword, Authentication Solutions Through Keystroke Dynamics, BioPassword, Issaquah, Wash, USA, 2006. 

6 Isao  Nakanishi, Shouta  Koike,  Yoshio  Itoh  and  Shigang  Li,  ”  DWT Domain  On-Line      Signature Veri?cation”, Tottori University, Japan, pp. 183-196 

7 Signature-Based Biometric Authentication (PDF Download Available). Available from: accessed Jan 22 2018.

8 Jain, Anil K., Arun Ross, and Salil Prabhakar. “An introduction to biometric recognition.” Circuits and Systems for Video Technology, IEEE Transactions on 14, no. 1 (2004): 4-20. 

9 Coskun, Baris, and Cormac Herley. “Can “Something You Know” Be Saved?” In Information Security, pp. 421-440. Springer Berlin Heidelberg, 2008.

10 Parmeshwar Manegopale, “A Survey on Palm print Recognition”, International Journal of Innovative Research in Science, Engineering and Technology, Vol.3, Issue 2, February 2014. 

11 L. Rabiner, B. H. Juang, “Fundamentals of Speech Recognition”, Pearson Education.

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13 Sulochana Sonkamble, DR. Ravindra Thool, Balwant Sonkamble , “Survey of biometric recognition systems and their applications”, 2005-10 JATIT.