Biometrics-Technology

Abstract

                In this Hi-Tech era, there is a great demand to identify and authenticate the individuals.  Till now we are totally dependent upon Passwords and Pin Numbers for identification. How secure are passwords? With the numerous passwords that an individual has to remember, they are often forgotten, misplaced, or stolen. Think of how many different passwords you have to remember: computer passwords, internet site logons and passwords, PIN numbers for the ATM and for credit cards, the list goes on. We are arriving at a conclusion that these technologies are not sufficient for the security of an individual as these are hard to remember, easily transferable, easily stolen and there are many weaknesses. Due to these weaknesses biometrics came into existence.

                 Biometrics is that study of science that deals with personal human behavioral and physiological characteristics and such as fingerprints, handprints, iris scanning, voice scanning, face recognition and signature recognition. These technologies are far more promising than that which are used currently to identify an individual. This paper highlights some of the benefits and the few limitations of using biometrics for authentication .With biometrics it doesn’t matter if we forget your password or lose your smart card.

                                                                                               

Introduction and History

Introduction:-

            The term "biometrics" is derived from the Greek words bio means “life” and metric means “to measure”.

          BIOMETRICS refers to the automatic identification of a person based on    his physiological / behavioral characteristics. This method of identification is preferred for various reasons; the person to be identified is required to be physically present at the point of identification; identification based on biometric techniques obviates the need to remember a password or carry a token.

            A biometric is a unique, measurable characteristic or trait for automatically recognizing or verifying the identity of a human being. Biometrics is a powerful combination of science and technology that can be used to protect and secure our most valuable information and property.      

            With the increased use of computers or vehicles of information technology, it is necessary to restrict access to sensitive or personal data.  By replacing PINs, biometric   techniques can   potentially prevent unauthorized access to fraudulent use of ATMs, cellular phones, smart cards, desktop PCs, workstations, and computer networks. PINs and passwords may be forgotten, and token based methods of identification like passports and driver’s licenses may be forged, stolen, or lost .Thus biometric   systems of   identification are   enjoying a renewed interest.

            Recognisation requires the system to look through many stored sets of characteristics and pick the one that matches the unknown individual being presented. Various types of biometric systems are being used for real–time identification; the most popular are based on   face   recognition   and fingerprint matching. However there are other biometric systems that utilize iris and retinal scan, speech, gesture recognisation, and hand geometry.

            Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions. The basic idea behind biometrics is that our bodies contain unique properties that can be used to distinguish us from others. A biometric system is essentially a pattern   recognition system, which makes a personal identification by determining the authenticity of a specific physiological or behavioral characteristics possessed by the user.

            An important issue in designing a practical system is to determine how an individual is identified. Depending on the context, a biometric system can be either a verification (authentication) system an identification system. Verification involves confirming or denying a person’s claimed identity. In identification one has to establish a person’s identity. Identification systems based on biometrics are important building blocks in simplifying our interaction with the myriad digital systems and devices that we

are all using in increasing numbers.

            There are levels of security from the most basic to the most robust with biometrics being the most secure:

Ø  Something that you have - such as an ID badge with a photograph on it.

Ø  Something that you know - such as a password or PIN number.

Something which you are - such as biometric data – fingerprints, iris, voice or face scans.   

Figure 1: Explains the meaning of definition

            Biometrics is rapidly evolving technology, which is being used in forensics such as criminal identification and prison security, and has the potential to be used in a large range of civilian application areas. Biometrics can be used transactions conducted via telephone and Internet (electronic commerce and electronic banking). In automobiles, biometrics can replace keys with key -less entry devices.

 History:-

                When we talk about biometric history, we would realize that since time immemorial people always tried their best to use some way or the other so that they could identify one person from another, whether it was through footprints or tattoos or photos. Biometric history indicates that the science did not originate at a single place. People all over the world were using the basics for mainly identifying individuals from each other.  

            The ancient Egyptians and the Chinese played a large role in biometrics' history. Although biometric technology seems to belong in the twenty-first century, the history of biometrics goes back thousands of years. Possibly the most primary known instance of biometrics in practice was a form of finger printing being used in China in the 14th century, as reported by explorer Joao de Barros. Barros wrote that the Chinese merchants were stamping children's palm prints and footprints on paper with ink so as to differentiate the young children from one another. This is one of the most primitive known cases of biometrics in use and is still being used today. 

             Bertillon developed a technique of multiple body measurements which later got named after him “Bertillonage”. His method was then used by police authorities throughout the world, until it quickly faded when it was discovered that some people shared the same measurements and based on the measurements alone, two people could get treated as one.  After the failure of Bertillonage, the police started using finger printing, which was developed by Richard Edward Henry of Scotland Yard, essentially reverting to the same methods used by the Chinese for years.  

            Commercial advancements for biometric devices began in the 1970s when a system called Identimat which measured the shape of the hand and length of the fingers was used as part of a time clock at Shearson Hamill, a Wall Street investment firm. Subsequently, hundreds of Identimat devices were used to establish identity for physical access at secure facilities run by Western Electric, U.S. Naval Intelligence, the Department of Energy, and U.S. Naval Intelligence and like organizations.

Block Diagram of Biometric System

            Biometric devices consist of a reader or scanning device, software that converts the gathered information into digital form, and a database that stores the biometric data for comparison with previous records. When converting the biometric input, the software identifies specific points of data as match points. The match points are processed using an algorithm into a value that can be compared with biometric data in the database.

            The biometric feature must have the following characteristics:-

(a) Universality, which means that every person should have the characteristic,

(b) Uniqueness, two persons should not have the same term or measurement of Characteristic.

(c) Permanence, the characteristic should be invariant with time.

(d) Measurability, the characteristic can be quantified that is the origin of the Cameras used in biometric systems are generally either CCD (charge couple device) or CMOS (combined metal oxide semiconductor) image sensors. CCD is comparatively more costly than CMOS.

Figure 2: Basic block diagram of biometrics system

            The main operations a system can perform are enrollment and test. During the enrollment, biometric information from an individual is stored. During the test, biometric information is detected and compared with the stored information. Note that it is crucial that storage and retrieval of such systems themselves be secure if the biometric system is, robust.

            The first block (sensor) is the interface between the real world and the system; it has to acquire all the necessary data. Most of the times it is an image acquisition system, but it can change according to the characteristics desired. A sample of the biometric trait is captured, processed by a computer, and stored for later comparison.

            The second block performs all the necessary pre-processing: it has to remove artifacts from the sensor, to enhance the input (e.g. removing background noise), to use some kind of normalization, etc.

             In the third block features needed are extracted. This step is an important step as the correct features need to be extracted and the optimal way. A vector of numbers or an image with particular properties is used to create a template. A template is a synthesis of all the characteristics extracted from the source, in the optimal size to allow for adequate identifiability. All Biometric authentications require comparing a registered or enrolled biometric sample (biometric template or identifier) against a newly captured biometric sample.

            If enrollment is being performed where the biometric system identifies a person from the entire enrolled population by searching a database for a match based solely on the biometric. For example, an entire database can be searched to verify a person has not applied for entitlement benefits under two different names. This is sometimes called “one-to-many” matching.

            If a verification phase is being performed, the biometric system authenticates a person’s claimed identity from their previously enrolled pattern. This is also called “one-to-one” matching. The obtained template is passed to a matcher that compares it with other existing templates. The matching program will analyze the template with the input. This will then be output for any specified use or purpose.

Classification of Biometrics

            Biometrics encompasses both physiological and behavioral characteristics. A physiological characteristic   are related to the shape of a body.  A relatively stable physical feature such as finger print,   hand   geometry, iris   pattern    or   facial features. These factors are basically unalterable without trauma to the individual.

            Behavioral tracts, on the other hand, are related to the behavior of a person. The most common trait used in identification is a person’s signature. Other behaviors   used include a person’s keyboard typing, gait and speech patterns. Most of the behavioral characteristics change over time.

            Some of physical biometrics is

Ø  Fingerprint - analyzing fingertip patterns.

Ø  Facial Recognition - measuring facial characteristics.

Ø  Hand Geometry - measuring the shape of the hand.

Ø  Iris recognition - analyzing features of colored ring of the eye.

            Some of behavioral biometrics is

Ø  Speaker Recognition - analyzing vocal behavior.

Ø  Signature Recognisation - analyze the physical activity of signing.

Ø  Gesture Recognisation - analyzing the motions of body.

Fingerprint:-

            Humans have used fingerprints for personal identification for many centuries and the matching accuracy using fingerprints has been shown to be very high. Fingerprinting is probably the best-known biometric- method of identification used for 100 years. There are a few variants of image capture technology available for such commercially oriented fingerprint sensor, including optical, silicon, ultrasound, thermal and hybrid.

            Among all the biometric techniques, fingerprint-based Identification is the oldest method that has been successfully used in numerous applications. Everyone is known to have unique, immutable fingerprints. A fingerprint is made of a series of ridges and furrows on the surface of the finger as shown in the fig 3.1.1. The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as minutiae points. Minutiae points are the local ridge characteristics that occur either at a ridge ending or a ridge bifurcation. A ridge ending is defined as the point where the ridge ends abruptly and the ridge bifurcation is the point where the ridge splits into two or more branches.

            When a user places their finger on the terminals scanner the image is electronically read, analyzed, and compared with a previously recorded image of the same finger which has been stored in the database. The imaging process is based on digital holography, using an electro-optical scanner about the size of a thumbprint. The scanner reads three-dimensional data from the finger such as skin undulations, and ridges and valleys, to create a unique pattern that is composed into a template file. An algorithm is developed to classify fingerprints into five classes, namely, whorl, right loop, arch and tented arch as shown in figure 3. Critical points in a finger print, called core and delta are marked on one of the fingers as shown in figure 3 (c). The core is the inner point, normally in the middle of the print, around which swirls, loops, or arches center. It is frequently characterized by a ridge ending and several acutely curved ridges. Deltas are the points, normally at the lower left and right hand of the fingerprint, around which a triangular series of ridges center.  The algorithm separates the number of ridges present in four directions (o degree, 45 degree, 90 degree and 135 degree) by filtering the central part of a fingerprint with a bank of Gabor filters.  This information is quantized to generate a finger code which is used for classification.  To avoid fake-finger attacks, some systems employ so-called liveness detection technology, which takes advantage of the sweat activity of human bodies. High-magnification lenses and special illumination technologies capture the finger’s perspiration and pronounce the finger dead or alive.

Advantages:-

Ø  Fingerprint recognition equipment is relatively low-priced compared to other biometric system.

Ø  Fingerprints are unique to each finger of each individual and the ridge arrangement remains permanent during one's lifetime.

Disadvantages:-

Ø  Some people have damaged or eliminated fingerprints.

Ø  Vulnerable to noise and distortion brought on by dirt and twists.

Face Recognisation:-

            Face recognition technology analyze the unique shape, pattern and positioning of the facial features. Face recognition is very complex technology and is largely software based. Face recognition starts with a picture, attempting to find a person in the image. This can be accomplished using several methods including movement, skin tones, or blurred human shapes. The face recognition system locates the head and finally the eyes of the individual. A matrix is then developed based on the characteristics of the individual’s face. The method of defining the matrix varies according to the algorithm (the mathematical process used by the computer to perform the comparison). This matrix is then compared to matrices that are in a database and a similarity score is generated for each comparison.

            Despite the fact that there are more reliable biometric recognition techniques such as fingerprint and iris recognition, these techniques are intrusive and their success depends highly on user cooperation, since the user must position her eye in front of the iris scanner or put her finger in the fingerprint device. On the other hand, face recognition is non-intrusive since it is based on images recorded by a distant camera, and can be very effective even if the user is not aware of the existence of the face recognition system. The human face is undoubtedly the most common characteristic used by humans to recognize other people and this is why personal identification based on facial images is considered the friendliest among all biometrics.

            Face has certain distinguishable landmarks that are the peaks and valleys that sum up the different facial features. There are about 80 peaks and valleys on a human face. The following are a few of the peaks and valleys that are measured by the software:

Ø  Distance between eyes

Ø  Width of nose

Ø  Depth of eye sockets

Ø  Cheekbones

Ø  Jaw line

Ø  Chin

            These peaks and valleys are measured to give a numerical code, a string of numbers, which represents the face in a database. This code is called a face print. Face recognition involves the comparison of a given face with other faces in a database with the objective of deciding if the face matches any of the faces in that database.

Image matching usually involves three steps:

1.      Detection of the face in a complex background and localization of its exact position,

2.      Extraction of facial features such as eyes, nose, etc, followed by normalization to align the face with the stored face images, and

3.      Face classification or matching.

  In addition, a face recognition system usually consists of the following four modules:

1. Sensor module, which captures face images of an individual. Depending on the sensor modality, the acquisition device maybe a black and white or color camera, a 3D sensor capturing range (depth) data, or an infrared camera capturing infrared images.
2. Face detection and feature extraction module. The acquired face images are first scanned to detect the presence of faces and find their exact location and size. The output of face detection is an image window containing only the face area. Irrelevant information, such as background, hair, neck and shoulders, ears, etc are discarded.
3. Classification module, in which the template extracted during step 2, is compared against the stored templates in the database to generate matching scores, which reveal how identical the faces in the probe and gallery images are. Then, a decision-making module either confirms (verification) or establishes (identification) the user’s identity based on the matching score. In case of face verification, the matching score is compared to a predefined threshold and based on the result of this comparison; the user is either accepted or rejected. In case of face identification, a set of matching scores between the extracted template and the templates of enrolled users is calculated. If the template of user X produces the best score, then the unknown face is more similar to X, than any other person in the database. To ensure that the unknown face is actually X and not an impostor, the matching score is compared to a predefined threshold.
4. Sometimes, more than one template per enrolled user is stored in the gallery database to account for different variations. Templates may also be updated over time, mainly to cope with variations due to aging.

            Face detection algorithms can be divided into three categories according to

1. Knowledge-based methods are based on human knowledge of the typical human face geometry and facial features arrangement. Taking advantage of natural face symmetry and the natural top-to-bottom and left-to-right order in which features appear in the human face, these methods find rules to describe the shape, size, texture and other characteristics of facial features (such as eyes, nose, chin, eyebrows) and relationships between them (relative positions and distances). A hierarchical approach may be used, which examines the face at different resolution levels. At higher levels, possible face candidates are found using a rough description of face geometry. At lower levels, facial features are extracted and an image region is identified as face or non-face based on predefined rules about facial characteristics and their arrangement.
2. Feature invariant approaches aim to find structural features that exist even when the viewpoint or lighting conditions vary and then use these to locate faces. Different structural features are being used: facial local features, texture, and shape and skin color. Local features such as eyes, eyebrows, nose, and mouth are extracted using multi-resolution or derivative filters, edge detectors, morphological operations or thresholding. Statistical models are then built to describe their relationships and verify the existence of a face. Neural networks, graph matching, and decision trees were also proposed to verify face candidates.
3. Template-based methods. To detect a face in a new image, first the head outline, which is fairly consistently roughly elliptical, is detected using filters or edge detectors. Then the contours of local facial features are extracted in the same way, exploiting knowledge of face and feature geometry.    

            More recently, techniques that rely on 3D shape data have been proposed. 3D face recognition is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. 3D face recognition has the potential to achieve better accuracy than its 2D counterpart by measuring geometry of rigid features on the face. This avoids such pitfalls of 2D face recognition algorithms as change in lighting, different facial expressions, make-up and head orientation. 

Advantages:-

Ø  No contact required.

Ø  Commonly available sensors (cameras).

Disadvantages:-

Ø  Face can be obstructed by hair, glasses, hats, scarves etc.

Ø  Difficult to distinguish between twins.

Ø  Sensitive to changes in lighting, expression, and poses faces changeover time.

Hand Geometry:-        

            Hand geometry recognition systems are based on a number of measurements taken from the human hand, including its shape, size of palm, and lengths and widths of the fingers. The technique is very simple, relatively easy to use, and inexpensive. Environmental factors such as dry weather or individual anomalies such as dry skin do not appear to have any negative effects on the verification accuracy of hand geometry-based systems. The geometry of the hand is not known to be very distinctive and hand geometry based recognition systems cannot be scaled up for systems requiring identification of an individual from a large population. Further, hand geometry information may not be invariant during the growth period of children. In addition, an individual's jewelry (e.g., rings) or limitations in dexterity (e.g., from arthritis), may pose further challenges in extracting the correct hand geometry information. The physical size of a hand geometry-based system is large, and it cannot be embedded in certain devices like laptops.

Advantages:-

Ø  Easy to capture.

Ø  The major advantage is that most people can use it and as such, the acceptance rate is good.

Ø  Believed to be a highly stable pattern over the adult lifespan.

Disadvantages:-

Ø  Use requires some training.

Ø  System requires a large amount of physical space.

Iris Recognisation:-

            The iris of each eye of each person is absolutely unique. In the entire human population, no two irises are alike in their mathematical detail. This even applies to identical twins. The iris of each eye is protected from the external environment. It is clearly visible from a distance, making it ideal for a biometric solution. Image acquisition for enrolment and recognition is easily accomplished and most importantly is non-intrusive.

            The Iris Code creation process starts with video-based image acquisition. This is a purely passive process achieved using CCD (Charge Coupled Device) Video Cameras. This image is then processed and encoded into an Iris Code record, which is stored in an Iris Code database. This stored record is then used for identification in any live transaction when an iris is presented for comparison.

 

Figure 6 : Iris scan process

            The iris-scan process begins with a photograph. A specialized camera, typically very close to the subject, no more than three feet, uses an infrared imager to illuminate the eye and capture a very high-resolution photograph. This process takes only one to two seconds and provides the details of the iris that are mapped, recorded and stored for future matching/verification.

            Eyeglasses and contact lenses present no problems to the quality of the image and the iris-scan systems test for a live eye by checking for the normal continuous fluctuation in pupil size.

            The inner edge of the iris is located by an iris-scan algorithm which maps the iris distinct patterns and characteristics. An algorithm is a series of directives that tell a biometric system how to interpret a specific problem. Algorithms have a number of steps and are used by the biometric system to determine if a biometric sample and record is a match.

            Iris is composed before birth and, except in the event of an injury to the eyeball, remains unchanged throughout an individual’s lifetime. Iris patterns are extremely complex, carry an astonishing amount of information and have over 200 unique spots. The fact that an individual’s right and left eyes are different and that patterns are easy to capture, establishes iris-scan technology as one of the biometrics that is very resistant to false matching and fraud.

            The false acceptance rate for iris recognition systems is 1 in 1.2 million, statistically better than the average fingerprint recognition system. The real benefit is in the false-rejection rate, a measure of authenticated users who are rejected. Fingerprint scanners have a 3 percent false-rejection rate, whereas iris scanning systems boast rates at the 0 percent level.

Advantages:-

Ø  Iris recognition is very accurate with very low false acceptance rate

 Disadvantages:-

Ø  Complex procedure.

Ø  High cost.

Speaker Recognition:-

            Speaker, or voice, recognition is a biometric modality that uses an individual’s voice for recognition purposes. The speaker recognition process relies on features influenced by both the physical structure of an individual’s vocal tract and the behavioral characteristics of the individual. A popular choice for remote authentication due to the availability of devices for collecting speech samples and its ease of integration, speaker recognition is different from some other biometric methods in that speech samples are captured dynamically or over a period of time, such as a few seconds. Analysis occurs on a model in which changes over time are monitored.

            Voice recognition technology utilizes the distinctive aspects of the voice to verify the identity of individuals. Voice recognition is occasionally confused with speech recognition, a technology which translates what a user is saying (a process unrelated to authentication). Voice recognition technology, by contrast, verifies the identity of the individual who is speaking. The two technologies are often bundled – speech recognition is used to translate the spoken word into an account number, and voice recognition verifies the vocal characteristics against those associated with this account. 

            Voice recognition can utilize any audio capture device, including mobile and land telephones and PC microphones. The performance of voice recognition systems can vary according to the quality of the audio signal as well as variation between enrollment and verification devices, so acquisition normally takes place on a device likely to be used for future verification.  During enrollment an individual is prompted to select a passphrase or to repeat a sequence of numbers. Voice recognition can function as a reliable authentication mechanism for automated telephone systems, adding security to automated telephone-based transactions in areas such as financial services and health care.  Certain voice recognition technologies are highly resistant to imposter attacks, means that voice recognition can be used to protect reasonably high-value transactions. 

Speech samples are waveforms with time on the horizontal axis and loudness on the vertical access. The speaker recognition system analyzes the frequency content of the speech and compares characteristics such as the quality, duration, intensity dynamics, and pitch of the signal.

            Voice recognition techniques can be divided into categories depending on the type of authentication domain.

• Fixed text method is a technique where the speaker is required to say a predetermined word that is recorded during registration on the system.

• In the text dependent method the system prompts the user to say a specific word or phrase, which is then computed on the basis of the user’s fundamental voice pattern.

• The text independent method is an advanced technique where the user need not articulate any specific word or phrase. The matching is done by the system on the basis of the fundamental voice patterns irrespective of the language and the text used.

Advantages:-

Ø  Simple and cost-effective technological application.

Ø  Can be used for remote authentication.

Disadvantages:-

Ø  Voice and language usage change over time (e.g. as a result of age or illness).

Signature Recognisation:-

            Biometric signature recognition systems measure and analyze the physical activity of signing. Important characteristics include stroke order, the pressure applied, the pen-up movements, the angle the pen is held, the time taken to sign, the velocity and acceleration of the signature. Some systems additionally compare the visual image of signatures, though the focus in signature biometrics lies on writer-specific information rather than visual handwritten content. While it may appear trivial to copy the appearance of a signature, it is difficult to mimic the process and behavior of signing.

Signature data can be captured via pens that incorporate sensors or through touch-sensitive surfaces which sense the unique signature characteristics. Touch-sensitive surfaces are increasingly being used on ICT devices such as screens, pads, mobile phones, laptops and tablet PCs.

 

 Advantages:-

Ø  Main uses of signature biometrics include limiting access to restricted documents and contracts, delivery acknowledgement and banking/finance related applications.

 Disadvantages:-

Ø  A person’s signature changes over time as well as under physical and emotional influences.

Gesture Recognisation System:-

            Gesture is the use of motions of the limbs or body as a means of expression, communicate an intention or feeling. Gesture recognition enables humans to interface with the machine (HMI) and interact naturally without any mechanical devices. Using the concept of gesture recognition, it is possible to point a finger at the computer screen so that the cursor will move accordingly. This could potentially make conventional input devices such as mouse, keyboards and even touch-screens redundant. The ability to track a person's movements and determine what gestures they may be performing can be achieved through various tools. Although there is a large amount of research done in image/video based gesture recognition, there is some variation within the tools and environments used between implementations. In order to capture human gestures by visual sensors, robust computer vision methods are also required, for example for hand tracking and hand posture recognition or for capturing movements of the head, facial expressions or gaze direction. The input devices of gesture recognisation system are

Ø  Depth-aware cameras: Using specialized cameras such as time-of-flight cameras, one can generate a depth map of what is being seen through the camera at a short range, and use this data to approximate a 3d representation of what is being seen. These can be effective for detection of hand gestures due to their short range capabilities.

Ø   Stereo cameras: Using two cameras whose relations to one another are known, a 3d representation can be approximated by the output of the cameras. To get the cameras' relations, one can use a positioning reference such as an infrared emitters.

Ø   Controller-based gestures: These controllers act as an extension of the body so that when gestures are performed, some of their motion can be conveniently captured by software. Mouse gestures are one such example

Ø   Single camera: A normal camera can be used for gesture recognition where the resources/environment would not be convenient for other forms of image-based recognition. Although not necessarily as effective as stereo or depth aware cameras, using a single camera allows a greater possibility of accessibility to a wider audience.

Advantages:-

Ø  A new interactive Technology.

Ø  Eliminates the use of mechanical devices.

 Disadvantages:-

Ø  Complex

Ø  High costs

 Multimodal Biometrics System:-

          Multimodal biometric systems are those that utilize more than one physiological or behavioral characteristic for enrollment, verification, or identification. A  biometric  system  which relies only  on a single  biometric identifier  in making  a personal identifications often not able to meet the desired performance requirements. Identification   based   on   multiple   biometrics   represents on emerging trend.  A multimodal  biometric  system  is  introduced which integrates face   recognition,  fingerprint  verification,  and  speaker  verification  in  making  a personal identification. This system takes advantage of the capabilities of each individual biometric. It can be  used  to  overcome  some  of  the  limitations  of  a  single biometrics.

Comparison of Biometric Technologies:-

 

 

 

Table 1: Comparison of Biometrics Technology

                In the above table, universality indicates how common the biometric is found in each person; uniqueness indicates how well the biometric separates one person from the other; permanence indicates how well the biometric resist the effect of aging; while collectability measures how easy it is to acquire the biometric for processing. Performance indicates the achievable accuracy, speed and robustness of the biometrics while acceptability indicates the degree of acceptance of the technology by the public in their daily life and circumvention indicates the level of difficulty to circumvent or fool the system into accepting an impostor.

 

 

APPLICATIONS

 Eye Gaze System:-

                        The Eye gaze Edge uses the pupil-center/corneal-reflection method to determine where the user is looking on the screen. An infrared-sensitive video camera, mounted beneath the System's screen, takes 60 pictures per second of the user's eye. A low power, infrared light emitting diode (LED), mounted in the center of the camera's lens illuminates the eye. The LED reflects a small bit of light off the surface of the eye's cornea. The light also shines through the pupil and reflects off of the retina, the back surface of the eye, and causes the pupil to appear white. The bright-pupil effect enhances the camera's image of the pupil so the system's image processing functions can locate the center of the pupil. The Edge calculates the person's gaze point, i.e., the coordinates of where he is looking on the screen, based on the relative positions of the pupil center and corneal reflection within the video image of the eye. Typically the Eye gaze Edge predicts the gaze point with an average accuracy of a quarter inch or better. Prior to operating the eye tracking applications, the Eye gaze Edge must learn several physiological properties of a user's eye in order to be able to project his gaze point accurately. The system learns these properties by performing a

calibration procedure. The user calibrates the system by fixing his gaze on a small circle displayed on the screen, and following it as it moves around the screen. The calibration procedure usually takes about 15 seconds, and the user does not need to recalibrate if he moves away from the Eye gaze Edge and returns later. A user operates the Eye gaze System by looking at rectangular keys that are displayed on the control screen.  To "press" an Eye gaze key, the user looks at the key for a specified period of time.  The gaze duration required to visually activate a key, typically a fraction of a second, is adjustable.  An array of menu

Conclusion and Future Works

Conclusion:-

            Biometric is an emerging area with many opportunities for growth. Biometrics is widely being used because of its user friendliness, flexibility in specifying required security level and long term stability. The technology will continue to improve and challenges such as interoperability solved through standardization. This will lead to increase in the market adoption rate and the technology will proliferate. Possibly in the near future, you will not have to remember PINs and passwords and keys in your bags or pockets will be things of the past.

 

 

Future works:-

            The future of biometrics holds great promise for law enforcement applications, as well for private industry uses. Biometrics’ future will include e-commerce applications for extra security on the checkout page, and biometrics will guard against unauthorized access to cars and cell phones. In the future, biometric technology will further develop 3-D infrared facial recognition access control, real-time facial recognition passive surveillance, and visitor management authentication systems. Already A4Vision, a provider of 3-D facial scanning and identification software uses specialized algorithms to interpret the traditional 2-D camera image and transfer it into a 3-D representation of a registered face. This makes it almost impossible to deceive the biometric system with still photos or other images. Strengthening existing biometric innovations for future growth all of these security innovations will make biometric technology more accurate and make its usage more widespread.