Category: biometrics

Physiological Modalities As depicted earlier, the physiological modalities are based on the direct measurement of parts of human body such as iris, fingerprint, shape, and position of fingers, etc. There are some physical traits which remain unaltered throughout a person’s life. They can be an excellent resource for identification of an individual. Fingerprint Recognition System It is the most known and used biometrics solution to authenticate people on biometric systems. The reasons for it being so popular are there are ten available sources of biometric and ease of acquisition. Every person has a unique fingerprint which is composed of ridges, grooves, and direction of the lines. There are three basic patterns of ridges namely, arch, loop, and whorl. The uniqueness of fingerprint is determined by these features as well as minutiae features such as bifurcation and spots (ridge endings). Fingerprint is one of oldest and most popular recognition technique. Fingerprint matching techniques are of three types − Minutiae Based Techniques − In these minutiae points are found and then mapped to their relative position on finger. There are some difficulties such as if image is of low quality, then it is difficult to find minutiae points correctly. Another difficulty is, it considers local position of ridges and furrows; not global. Correlation Based Method − It uses richer gray scale information. It overcomes problems of minutiae-based method, by being able to work with bad quality data. But it has some of its own problems like localization of points. Pattern Based (Image Based) Matching − Pattern based algorithms compare the basic fingerprint patterns (arch, whorl, and loop) between a stored template and a candidate fingerprint. Merits of Finger Recognition System It is the most contemporary method. It is most economical method. It is highly reliable and secure. It works on a small template size, which speeds up the verifying process. It consumes less memory space. Demerits of Finger Recognition System Scars, cuts or absence of finger can hinder the recognition process. The systems can be fooled by using artificial finger made of wax. It involves physical contact with the system. They leave the pattern of finger behind at the time of entering sample. Applications of Finger Recognition System Verification of driver-license authenticity. Checking validity of driving license. Border Control/Visa Issuance. Access control in organizations. Facial Recognition System Facial recognition is based on determining shape and size of jaw, chin, shape and location of the eyes, eyebrows, nose, lips, and cheekbones. 2D facial scanners start reading face geometry and recording it on the grid. The facial geometry is transferred to the database in terms of points. The comparison algorithms perform face matching and come up with the results. Facial recognition is performed in the following ways − Facial Metrics − In this type, the distances between pupils or from nose to lip or chin are measured. Eigen faces − It is the process of analyzing the overall face image as a weighted combination of a number of faces. Skin Texture Analysis − The unique lines, patterns, and spots apparent in a person’s skin are located. Merits of Facial Recognition System It offers easy storage of templates in database. It reduces the statistic complexities to recognize face image. It involves no physical contact with the system. Demerits of Facial Recognition System Facial traits change over time. Uniqueness is not guaranteed, for example, in case of identical twins. If a candidate face shows different expressions such as light smile, then it can affect the result. It requires adequate lighting to get correct input. Applications of Facial Recognition System General Identity Verification. Verification for access control. Human-Computer Interaction. Criminal Identification. Surveillance. Iris Recognition System Iris recognition works on the basis of iris pattern in human eye. The iris is the pigmented elastic tissue that has adjustable circular opening in center. It controls the diameter of pupil. In adult humans, the texture of iris is stable throughout their lives. The iris patterns of left and right eyes are different. The iris patterns and colors change from person to person. It involves taking the picture of iris with a capable camera, storing it, and comparing the same with the candidate eyes using mathematical algorithms. Merits of Iris Recognition System It is highly accurate as the chance of matching two irises is 1 in 10 billion people. It is highly scalable as the iris pattern remains same throughout a person’s lifetime. The candidate need not remove glasses or contact lenses; they do not hamper the accuracy of the system. It involves no physical contact with the system. It provides instant verification (2 to 5 seconds) because of its small template size. Demerits of Iris Recognition System Iris scanners are expensive. High quality images can fool the scanner. A person is required to keep his/her head very still for accurate scanning. Applications of Iris Recognition System National security and Identity cards such as Adhaar card in India. Google uses iris recognition for accessing their datacenters. Hand Geometry Recognition System It includes measuring length and width of palm, surface area, length and position of fingers, and overall bone structure of the hand. A person’s hand is unique and can be used to identify a person from others. There are two Hand Geometry systems − Contact Based − a hand is placed on a scanner’s surface. This placement is positioned by five pins, which guide the candidate hand to position correctly for the camera. Contact Less − In this approach neither pins nor platform are required for hand image acquisition. Merits of Hand Geometry Recognition System It is sturdy and user friendly. The changes in skin moisture or texture do not affect the result. Demerits of Hand Geometry Recognition System Since the hand geometry is not unique, it is not very reliable. It is effective in case of adults and not for the growing children. If candidate’s hand is with jewelry, plaster, or arthritis, it is likely to introduce a problem. Applications of Hand Geometry Recognition System Nuclear power plants and

Biometrics – Overview The term Biometrics is composed of two words − Bio (Greek word for Life) and Metrics (Measurements). Biometrics is a branch of information technology that aims towards establishing one’s identity based on personal traits. Biometrics is presently a buzzword in the domain of information security as it provides high degree of accuracy in identifying an individual. What is Biometrics? Biometrics is a technology used to identify, analyze, and measure an individual’s physical and behavioral characteristics. Each human being is unique in terms of characteristics, which make him or her different from all others. The physical attributes such as finger prints, color of iris, color of hair, hand geometry, and behavioral characteristics such as tone and accent of speech, signature, or the way of typing keys of computer keyboard etc., make a person stand separate from the rest. This uniqueness of a person is then used by the biometric systems to − Identify and verify a person. Authenticate a person to give appropriate rights of system operations. Keep the system safe from unethical handling. What is a Biometric System? A biometric system is a technology which takes an individual’s physiological, behavioral, or both traits as input, analyzes it, and identifies the individual as a genuine or malicious user. Evolution of Biometrics The idea of biometrics was present since few years from now. In 14th century, China practiced taking finger prints of merchants and their children to separate them from all others. Fingerprinting is still used today. In the 19th century, an Anthropologist named Alphonse Bertillion developed a method (named Bertillionage) of taking body measurements of persons to identify them. He had realized that even if some features of human body are changed, such as length of hair, weight, etc., some physical traits of body remain unchanged, such as length of fingers. This method diminished quickly as it was found that the persons with same body measurements alone can be falsely taken as one. Subsequently, Richard Edward Henry from Scotland Yard developed a method for fingerprinting. The idea of retinal identification was conceived by Dr. Carleton Simon and Dr. Isadore Goldstein in 1935. In 1976, a research and development effort was put in at EyeDentify Inc. The first commercial retina scanning system was made available in 1981. Iris recognition was invented by John Daugman in 1993 at Cambridge University. In 2001, Biometrics Automated Toolset (BAT) was introduced in Kosovo, which provided a concrete identification means. Today, biometric has come up as an independent field of study with precise technologies of establishing personal identities. Why Biometrics is Required? With increasing use of Information Technology in the field of banking, science, medication, etc., there is an immense need to protect the systems and data from unauthorized users. Biometrics is used for authenticating and authorizing a person. Though these terms are often coupled; they mean different. Authentication (Identification) This process tries to find out answer of question, “Are you the same who you are claiming to be?”, or, “Do I know you?” This is one-to-many matching and comparison of a person’s biometrics with the whole database. Verification This is the one-to-one process of matching where live sample entered by the candidate is compared with a previously stored template in the database. If both are matching with more than 70% agreeable similarity, then the verification is successful. Authorization It is the process of assigning access rights to the authenticated or verified users. It tries to find out the answer for the question, “Are you eligible to have certain rights to access this resource?” Shortcomings of Conventional Security Aids The conventional methods of information system security used ID cards, passwords, Personal Identification Numbers (PINs), etc. They come with the following disadvantages − They all mean recognizing some code associated with the person rather than recognizing the person who actually produced it. They can be forgotten, lost, or stolen. They can be bypassed or easily compromised. They are not precise. In such cases, the security of the system is threatened. When the systems need high level of reliable protection, biometrics comes to help by binding the identity more oriented to individual. Basic Components of a Biometric System In general, a biometric system can be divided into four basic components. Let us see them briefly − Input Interface (Sensors) It is the sensing component of a biometrics system that converts human biological data into digital form. For example, A Metal Oxide Semiconductor (CMOS) imager or a Charge Coupled Device (CCD) in the case of face recognition, handprint recognition, or iris/retinal recognition systems. An optical sensor in case of fingerprint systems. A microphone in case of voice recognition systems. Processing Unit The processing component is a microprocessor, Digital Signal Processor (DSP), or computer that processes the data captured from the sensors. The processing of the biometric sample involves − Sample image enhancement Sample image normalization Feature extraction Comparison of the biometric sample with all stored samples in database. Database Store The database stores the enrolled sample, which is recalled to perform a match at the time of authentication. For identification, there can be any memory from Random Access Memory (RAM), flash EPROM, or a data server. For verification, a removable storage element like a contact or contactless smart card is used. Output Interface The output interface communicates the decision of the biometric system to enable the access to the user. This can be a simple serial communication protocol RS232, or the higher bandwidth USB protocol. It could also be TCP/IP protocol, Radio Frequency Identification (RFID), Bluetooth, or one of the many cellular protocols. General Working of a Biometric System There are four general steps a biometric system takes to perform identification and verification − 1. Acquire live sample from candidate. (using sensors) 2. Extract prominent features from sample. (using processing unit) 3. Compare live sample with samples stored in database. (using algorithms) 4. Present the decision. (Accept or reject the candidate.) The biometric sample is acquired from candidate user. The prominent features are extracted from the

Multimodal Biometric Systems All the biometric systems we discussed till now were unimodal, which take single source of information for authentication. As the name depicts, multimodal biometric systems work on accepting information from two or more biometric inputs. A multimodal biometric system increases the scope and variety of input information the system takes from the users for authentication. Why Multimodal Biometrics is Required? The unimodal systems have to deal with various challenges such as lack of secrecy, non-universality of samples, extent of user’s comfort and freedom while dealing with the system, spoofing attacks on stored data, etc. Some of these challenges can be addressed by employing a multimodal biometric system. There are several more reasons for its requirement, such as − Availability of multiple traits makes the multimodal system more reliable. A multimodal biometric system increases security and secrecy of user data. A multimodal biometric system conducts fusion strategies to combine decisions from each subsystem and then comes up with a conclusion. This makes a multimodal system more accurate. If any of the identifiers fail to work for known or unknown reasons, the system still can provide security by employing the other identifier. Multimodal systems can provide knowledge about “liveliness” of the sample being entered by applying liveliness detection techniques. This makes them capable to detect and handle spoofing. Working of Multimodal Biometric System Multimodal biometric system has all the conventional modules a unimodal system has − Capturing module Feature extraction module Comparison module Decision making module In addition, it has a fusion technique to integrate the information from two different authentication systems. The fusion can be done at any of the following levels − During feature extraction. During comparison of live samples with stored biometric templates. During decision making. The multimodal biometric systems that integrate or fuse the information at initial stage are considered to be more effective than the systems those integrate the information at the later stages. The obvious reason to this is, the early stage contains more accurate information than the matching scores of the comparison modules. Fusion Scenarios in Multimodal Biometric System Within a multimodal biometric system, there can be variety in number of traits and components. They can be as follows − Single biometric trait, multiple sensors. Single biometric trait, multiple classifiers (say, minutiae-based matcher and texture-based matcher). Single biometric trait, multiple units (say, multiple fingers). Multiple biometric traits of an individual (say, iris, fingerprint, etc.). These traits are then operated upon to confirm user’s identity. Design Issues with Multimodal Biometric Systems You need to consider a number of factors while designing a multimodal biometric system − Level of security you need to bring in. The number of users who will use the system. Types of biometric traits you need to acquire. The number of biometric traits from the users. The level at which multiple biometric traits need integration. The technique to be adopted to integrate the information. The trade-off between development cost versus system performance. Learning working make money

Biometric System Security The operations of a biometric system depend heavily on the input devices that are subjected to operational limitations. At times, the devices themselves may fail to capture the necessary input samples. They may not capture the sample sufficiently. This makes the system unreliable and vulnerable. The more vulnerable a biometric system is, the more insecure it is. Biometric System Vulnerability There are the two major causes of biometric system vulnerability − System Failures There are two ways in which a biometric system can fail to work − Intrinsic failures − They are failures such as non-working sensors, failure of feature extraction, matching, or decision making modules, etc. Failures due to attacks − They are due to loopholes in the biometric system design, availability of any computations to the attackers, insider attacks from unethical system administrators, etc. Non-secure Infrastructure The biometric system can be accessible to malicious users if its hardware, software, and user data are not safeguarded. Risks with Biometric System Security The security of a biometric system is important as the biometric data is not easy to revoke or replace. There are following prominent risks regarding security of biometric systems − Risk of User Data Being Stolen If the biometric system is vulnerable, the hacker can breach the security of it and collect the user data recorded in the database. It creates more hazards to privacy. Risk of User Data Getting Compromised After acquiring the biometric sample, the hacker can present a fake sample to the system. If user data is compromised, it remains compromised forever. The obvious reason is, user has only a limited number of biometrics and they are difficult to replace, unlike passwords or ID cards. Though biometric data is encrypted and stored, it needs to be decrypted for matching purpose. At the time of matching a hacker may breach the security. Biometric System Security A number of solutions are proposed to address the biometric system security issue. Biometric templates are never stored in the raw form. They are encrypted; sometimes even twice. In the case of biometrics, there are various resources involved such as humans (subjects or candidates), entities (system components or processes), and biometric data (information). The security requirements of confidentiality, integrity, authenticity, non-repudiation, and availability are essential in biometrics. Let us go through them briefly − Authenticity It is the quality or the state of being pure, genuine, or original, rather than being reproduced. Information is authentic when it is in the same state and quality when it was created, stored, or transferred. There are two authenticities in a biometric system − entity authenticity and data origin authenticity. Entity authenticity confirms that all entities involved in the overall processing are the ones they claim to be. Data origin authenticity ensures genuineness and originality of data. For example, the biometrics data is captured with sensor devices. The captured data that came from a genuine sensor is not spoofed from a previous recording. Confidentiality It is limiting information access and disclosure to authorized users and preventing access by or disclosure to unauthorized people. In cases of a biometric system, it mainly refers to biometric and related authentication information when it is captured and stored, which needs to be kept secret from unauthorized entities. The biometric information should only be accessible completely to the person it belongs. During identification and variation, the accessing candidate needs to be restricted with appropriate security measures. Integrity It is the condition of being complete and unaltered that refers to its consistency, accuracy, and correctness. For a biometric system, the integrity should be high. Any malicious manipulations during operation and storage should be kept away or detected earliest by including its notification and correction. Non-repudiation It is identification of involved resources such as entities and components. It is also seen as accountability. For example, it prohibits a sender or a recipient of biometric information from denying having sent or received biometric information. Availability A resource has the property of availability with respect to a set of entities if all members of the set can access the resource. An aspect called reachability ensures that the humans or system processes either can or cannot be contacted, depending on user interests. Attackers can make the system unusable for genuine users, thus preventing them from using authenticated applications. These attackers target the availability of the information. Criteria for Generating Biometric Templates Here are the criteria for generating biometric templates − Ensuring that the template comes from a human candidate and is captured by a genuine sensor and software. Securing a biometric template by encryption with irreversibility properties. This makes it difficult for hackers to compute the original biometric information from secure template. Creating an unlikable (unique) biometric template. A biometric system should not be able to access the template of the same candidate recorded into another biometric system. In case if a hacker manages to retrieve a biometric template from one biometric system, he should not be able to use this template to gain access through another biometric system even though both verifications may be based on the same biometric template of the candidate. Further, an unlinkable biometric system should make it impossible to derive any information based on the relation between two templates. Creating a cancellable and renewable template. It emphasizes on the ability to cancel or deactivate the compromised template and reproduce another one, in a similar manner that a lost or stolen smartcard can be reproduced. The ‘renewable’ and ‘unlinkable’ characteristics are achieved through salting techniques. Salting adds randomly generated unique data known as ‘salt’ to the original information to make it distinct from the others. Designing a biometric system accuracy with respect to both FAR and FRR. Selecting a suitable encryption algorithm carefully. Some algorithms may amplify even small variations inherent in an individual’s biometric data, which can lead to higher FRR. Using an important encryption technique such as hashing method, which is effective when a different permutation is applied with each template generation. Different permutations ensure

Behavioral Modalities Behavioral biometrics pertains to the behavior exhibited by people or the manner in which people perform tasks such as walking, signing, and typing on the keyboard. Behavioral biometrics modalities have higher variations as they primarily depend on the external factors such as fatigue, mood, etc. This causes higher FAR and FRR as compared to solutions based on a physiological biometrics. Gait Recognition Gait is the manner of a person’s walking. People show different traits while walking such as body posture, distance between two feet while walking, swaying, etc., which help to recognize them uniquely. A gait recognition based on the analyzing the video images of candidate’s walk. The sample of candidate’s walk cycle is recorded by Video. The sample is then analyzed for position of joints such as knees and ankles, and the angles made between them while walking. A respective mathematical model is created for every candidate person and stored in the database. At the time of verification, this model is compared with the live sample of the candidate walk to determine its identity. Merits of Gait Recognition System It is non-invasive. It does not need the candidate’s cooperation as it can be used from a distance. It can be used for determining medical disorders by spotting changes in walking pattern of a person in case of Parkinson’s disease. Demerits of Gait Recognition System For this biometric technique, no model is developed with complete accuracy till now. It may not be as reliable as other established biometric techniques. Application of Gait Recognition System It is well-suited for identifying criminals in the crime scenario. Signature Recognition System In this case, more emphasis is given on the behavioral patterns in which the signature is signed than the way a signature looks in terms of graphics. The behavioral patterns include the changes in the timing of writing, pauses, pressure, direction of strokes, and speed during the course of signing. It could be easy to duplicate the graphical appearance of the signature but it is not easy to imitate the signature with the same behavior the person shows while signing. This technology consists of a pen and a specialized writing tablet, both connected to a computer for template comparison and verification. A high quality tablet can capture the behavioral traits such as speed, pressure, and timing while signing. During enrollment phase, the candidate must sign on the writing tablet multiple times for data acquisition. The signature recognition algorithms then extracts the unique features such as timing, pressure, speed, direction of strokes, important points on the path of signature, and the size of signature. The algorithm assigns different values of weights to those points. At the time of identification, the candidate enters the live sample of the signature, which is compared with the signatures in the database. Constraints of Signature Recognition System To acquire adequate amount of data, the signature should be small enough to fit on tablet and big enough to be able to deal with. The quality of the writing tablet decides the robustness of signature recognition enrollment template. The candidate must perform the verification processes in the same type of environment and conditions as they were at the time of enrollment. If there is a change, then the enrollment template and live sample template may differ from each other. Merits of Signature Recognition System Signature recognition process has a high resistance to imposters as it is very difficult to imitate the behavior patterns associated with the signature. It works very well in high amount business transactions. For example, Signature recognition could be used to positively verify the business representatives involved in the transaction before any classified documents are opened and signed. It is a non-invasive tool. We all use our signature in some sort of commerce, and thus there are virtually no privacy rights issues involved. Even if the system is hacked and the template is stolen, it is easy to restore the template. Demerits of Signature Recognition System The live sample template is prone to change with respect to the changes in behavior while signing. For example, signing with a hand held in plaster. User need to get accustomed of using signing tablet. Error rate is high till it happens. Applications of Signature Recognition System It is used in document verification and authorization. The Chase Manhattan Bank, Chicago is known as the first bank to adopt Signature Recognition technology. Keystroke Recognition System During the World War II, a technique known as Fist of the Sender was used by military intelligence to determine if the Morse code was sent by enemy or ally based on the rhythm of typing. These days, keystroke dynamics the easiest biometric solution to implement in terms of hardware. This biometric analyzes candidate’s typing pattern, the rhythm, and the speed of typing on a keyboard. The dwell time and flight time measurements are used in keystroke recognition. Dwell time − It is the duration of time for which a key is pressed. Flight time − It is the time elapsed between releasing a key and pressing the following key. The candidates differ in the way they type on the keyboard as the time they take to find the right key, the flight time, and the dwelling time. Their speed and rhythm of typing also varies according to their level of comfort with the keyboard. Keystroke recognition system monitors the keyboard inputs thousands of times per second in a single attempt to identify users based on their habits of typing. There are two types of keystroke recognition − Static − It is one time recognition at the start of interaction. Continuous − It is throughout the course of interaction. Application of Keystroke Dynamics Keystroke Recognition is used for identification/verification. It is used with user ID/password as a form of multifactor authentication. It is used for surveillance. Some software solutions track keystroke behavior for each user account without end-user’s knowledge. This tracking is used to analyze if the account was being shared or used by anyone

Biometrics and Image Processing Images have a huge share in this era of information. In biometrics, image processing is required for identifying an individual whose biometric image is stored in the database previously. Faces, fingerprints, irises, etc., are image-based biometrics, which require image processing and pattern recognition techniques. For an image based biometric system to work accurately, it needs to have the sample image of user’s biometric in a very clear and non-adulterated form. Requirement of Image Processing in Biometrics The image of user’s biometric is fed into the biometric system. The system is programmed to manipulate the image using equations, and then store the results of the computation for each pixel. To selectively enhance certain fine features in the data and to remove certain noise, the digital data is subjected to various image processing operations. Image processing methods can be grouped into three functional categories − Image Restoration Image restoration mainly includes − Reducing noise introduced in the image at the time of acquiring sample. Removing distortions appeared during enrollment of biometric. Image smoothing reduces noise in the image. Smoothing is carried out by replacing each pixel by the average value with the neighboring pixel. The biometric system uses various filtering algorithms and noise reduction techniques such as Median Filtering, Adaptive Filtering, Statistical Histogram, Wavelet Transforms, etc. Image Enhancement Image enhancement techniques improve the visibility of any portion or feature of the image and suppress the information in other parts. It is done only after restoration is completed. It includes brightening, sharpening, adjusting contrast, etc., so that the image is usable for further processing. Feature Extraction Two types of features are extracted from image, namely − General features − The features such as shape, texture, color, etc., which are used to describe content of the image. Domain-specific features − They are application dependent features such as face, iris, fingerprint, etc. Gabor filters are used to extract features. When the features are extracted from the image, you need to choose a suitable classifier. The widely used classifier Nearest Neighbor classifier, which compares the feature vector of the candidate image with the vector of the image stored in the database. B-Splines are approximations applied to describe curve patterns in fingerprint biometric systems. The coefficients of B-Splines are used as features. In case of iris recognition system, the images of iris are decomposed using Discrete Wavelet Transform (DWT) and the DWT coefficients are then used as features. Learning working make money