BIO- METRIC SECURITY SYSTEM FULL REPORT

Bio metrics refers to the automatic identification of a person based on his/her physiological or behavioral characteristics. This method of identification is preferred over traditional methods involving passwords and PIN numbers 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.

With the increased use of computers as vehicles of information technology, it is necessary to restrict access to sensitive/personal data. By replacing PINs, biometric techniques can potentially prevent unauthorized access to or 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. 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, facial thermograms, and hand geometry.

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 or an identification system.

Verification vs Identification:

There are two different ways to resolve a person's identity: verification and identification. Verification (Am I whom I claim I am?) involves confirming or denying a person's claimed identity. In identification, one has to establish a person's identity (Who am I?). Each one of these approaches has it's own complexities and could probably be solved best by a certain biometric system.

Applications:

Biometrics is a rapidly evolving technology which is being widely 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 to prevent unauthorized access to ATMs, cellular phones, smart cards, desktop PCs, workstations, and computer networks. It can be used during transactions conducted via telephone and internet (electronic commerce and electronic banking). In automobiles, biometrics can replace keys with key-less entry devices.

How They Work:

Although many technologies fit in the biometric space, each works a bit differently. The fingerprint scanners shine a light through a prism that reflects off your finger to a charge-coupled device (CCD), creating an image that gets processed by an 

onboard computer. It's important to note that the actual fingerprint image is not recorded. Instead, the devices perform a reduction of

the image to data points, called minutiae, that describe the fingerprint layout, called a template.

Voice authenticators use a telephone or microphone to record a user's voice pattern, then use that pattern to validate the person. Since these software systems rely on very low-cost devices, they are generally the least expensive systems to implement for large numbers of users. The standard caveats learned from voice dictation systems apply here. These devices must be able to work with background noise and the variability of off-the-shelf microphones.

Relatively new on the biometric scene, face recognition devices use PC-attached cameras to record facial geometry. Visionics' FaceIt NT requires an analog camera with a frame-grabber card that must perform at high speed, while Miros's TrueFace Network works with any videoconferencing camera.

Once the biometric data is collected, it is encrypted and stored--locally, in the case of the desktop-only products; in a central database for the network solutions.

When a user tries to log on, the software compares the incoming biometric data against the stored data.

Among all the biometric techniques, fingerprint-based identification is the oldest method which 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. The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points. Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or a ridge ending.Fingerprint matching techniques can be placed into two categories: minutae-based and correlation based. Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. However, there are some difficulties when using this approach. It is difficult to extract the minutiae points accurately when the fingerprint is of low quality. Also this method does not take into account the global pattern of ridges and furrows. The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. However, it has some of its own shortcomings. Correlation-based techniques require the precise location of a registration point and are affected by image translation and rotation.

Fingerprint matching based on minutiae has problems in matching different sized (unregistered) minutiae patterns. Local ridge structures cannot be completely characterized by minutiae. Efforts are being on to try an alternate representation of fingerprints, which will capture more local information and yield a fixed length code for the fingerprint. The matching will then hopefully become a relatively simple task of calculating the Euclidean distance will between the two codes.

Scientists are developing algorithms which are more robust to noise in fingerprint images and deliver increased accuracy in real-time. A commercial fingerprint-based authentication system requires a very low False Reject Rate (FAR) for a given False 

Accept Rate (FAR). This is very difficult to achieve with any one technique. Scientists are investigating methods to pool evidence

from various matching techniques to increase the overall accuracy of the system. In a real application, the sensor, the acquisition system and the variation in performance of the system over time is very critical. Scientists are also field testing this system on a limited number of users to evaluate the system performance over a period of time.

Fingerprint Classification:

Large volumes of fingerprints are collected and stored everyday in a wide range of applications including forensics, access control, and driver license registration. An automatic recognition of people based on fingerprints requires that the input fingerprint be matched with a large number of fingerprints in a database (FBI database contains approximately 70 million fingerprints!).

To reduce the search time and computational complexity, it is desirable to classify these fingerprints in an accurate and consistent manner so that the input fingerprint is required to be matched only with a subset of the fingerprints in the database.

Fingerprint classification is a technique to assign a fingerprint into one of the several pre-specified types already established in the literature, which can provide an indexing mechanism. Fingerprint classification can be viewed as a coarse level matching of the fingerprints. An input fingerprint is first matched at a coarse level to one of the pre-specified types and then, at a finer level, it is compared to the subset of the database containing that type of fingerprints only. Different algorithms are developed to classify fingerprints into five classes, namely, whorl, right loop, left loop, arch, and tented arch. The algorithm separates the number of ridges present in four directions (0 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. This classification is based on a two-stage classifier which uses a K-nearest neighbor classifier in the first stage and a set of neural networks in the second stage. The classifier is tested on 4,000 images in the NIST-4 database. For the five-class problem, classification accuracy of 90% is achieved. For the four-class problem (arch and tented arch

combined into one class), we are able to achieve a classification accuracy of 94.8%. By incorporating a reject option, the classification accuracy can be increased to 96% for the five-class classification and to 97.8% for the four-class classification when 30.8% of the images are rejected.

Fingerprint Image Enhancement:

A critical step in automatic fingerprint matching is to automatically and reliably extract minutiae from the input fingerprint images. However, the performance of a minutiae extraction algorithm relies heavily on the quality of the input fingerprint images.

In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of the fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. Scientists have developed a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and furrow structures of input fingerprint images based on the estimated local ridge orientation and frequency. Scientists have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithms improves both the goodness index and the verification accuracy.

Hand Geometry:

This approach uses the geometric shape of the hand for authenticating a user's identity. Authentication of identity using hand geometry is an interesting problem. Individual hand features are not descriptive enough for identification. However, it is possible to devise a method by combining various individual features to attain robust verification.

Hand Geometry vs Fingerprints:

Unlike fingerprints, the human hand isn't unique. One can use finger length, thickness, and curvature for the purposes of verification but not for identification.

For some kinds of access control like immigration and border control, invasive biometrics (e.g., fingerprints) may not be desirable as they infringe on privacy. In such situations it is desirable to have a biometric system that is sufficient for verification. As hand geometry is not distinctive, it is

the ideal choice.

Furthermore, hand geometry data is easier to collect. With fingerprint collection good frictional skin is required by imaging systems, and with retina-based recognition systems, special lighting is necessary. Additionally, hand geometry can be easily combined with other biometrics, namely fingerprint. One can envision a system where fingerprints are used for (infrequent) identification and hand geometry is used for (frequent) verification.

Some of the currently available software performs two basic functions:

· Capturing Hand Images, and

· Extracting Features:

The image acquisition system comprises of a light source, a camera, a single mirror and a flat surface (with five pegs on it). The user places his hand - palm facing downwards - on the flat surface of the device. The five pegs serve as control points for an appropriate placement of the right hand of the user. The device also has knobs to change the intensity of the light source and the focal length of the camera. The lone mirror projects the side-view of the user's hand onto the camera. The device is hooked to a PC with a GUI application, which provides a live visual feedback of the top-view and the side-view of the hand. The GUI aids in capturing the hand image.

Feature extraction involves computing the widths and lengths of the fingers at various locations using the captured image. These metrics define the feature vector of the user's hand. Current

research work involves identifying new features that would result in better discriminability between two different hands, and designing a deformable model for the hand.

Hand-scan is a relatively accurate technology, but does not draw on as rich a data set as finger, face, or Voice. A decent measure of the distinctiveness of a biometric technology is its ability to perform 1-to-many searches - that is, the ability to identify a user without the user first claiming an identity. Hand-scan does not perform 1-to-many identification, as similarities between hands are not uncommon. Where hand-scan does have an advantage is in its FTE (failure to enroll) rates, which measure the likelihood that a user is incapable of enrolling in the system. Finger-scan, by comparison, is prone to FTE's due to poor quality fingerprints; facial-scan requires consistent lighting to properly enroll a user. Since nearly all users will have the dexterity to use hand-scan technology, fewer employees and visitors will need to be processes outside the biometric.

Speaker Verification

The speaker-specific characteristics of speech are due to differences in physiological and behavioral aspects of the speech production system in humans. The main physiological aspect of the human speech production system is the vocal tract shape. The vocal tract is generally considered as the speech production organ above the vocal folds, which consists of the following: (i) laryngeal pharynx (beneath the epiglottis), (ii) oral pharynx (behind the tongue, between the epiglottis and velum), (iii) oral cavity (forward of the velum and bounded by the lips, tongue, and palate), 

 (iv) nasal pharynx (above the velum, rear end of nasal cavity), and (v) nasal cavity (above the palate and extending from the pharynx to the nostrils).

The vocal tract modifies the spectral content of an acoustic wave as it passes through it, thereby producing speech. Hence, it is common in speaker verification systems to make use of features derived only from the vocal tract. In order to characterize the features of the vocal tract, the human speech production mechanism is represented as a discrete-time system of the form depicted in following diagram:

The acoustic wave is produced when the airflow from the lungs is carried by the trachea through the vocal folds. This source of excitation can be characterized as phonation, whispering, frication, compression, vibration, or a combination of these. Phonated excitation occurs when the airflow is modulated by the vocal folds. Whispered excitation is produced by airflow rushing through a small triangular opening between the arytenoids cartilage at the rear of the nearly closed vocal folds. Frication excitation is produced by constrictions in the vocal tract. Compression excitation results from releasing a completely closed and pressurized vocal tract. Vibration excitation is caused by air being forced through a closure other than the vocal folds, especially at the tongue. Speech produced by phonated excitation is called voiced, that produced by phonated excitation plus frication is called mixed voiced, and that produced by other types of excitation is called unvoiced.

It is possible to represent the vocal-tract in a parametric form as the transfer function H(z). In order to estimate the parameters of H(z) from the observed speech waveform, it is necessary to assume some form for H(z). Ideally, the transfer function should contain poles as well as zeros. However, if only the voiced regions of 

speech are used then an all-pole model for H(z) is sufficient. Furthermore, linear prediction analysis can be used to efficiently estimate the parameters of an all-pole model. Finally, it can also be noted that the all-pole model is the minimum-phase part of the true model and has an identical magnitude spectra, which contains the bulk of the speaker-dependent information.

The above discussion also underlines the text-dependent nature of the vocal-tract models. Since the model is derived from the observed speech, it is dependent on the speech. Following Figure illustrates the differences in the models for two speakers saying the same vowel.

Speaker Modeling :

Utterances spoken by the same person but at different times result in similar yet a different sequence of feature vectors. The purpose of voice modeling is to build a model that captures these variations in the extracted set of features. There are two types of models that have been used extensively in speaker verification and speech recognition systems: stochastic models and template models. The stochastic model treats the speech production process as a parametric random process and assumes that the parameters of the underlying stochastic process can be estimated in a precise, well-defined manner. The template model attempts to model the speech production process in a non-parametric manner by retaining a number of sequences of feature vectors derived from multiple utterances of the same word by the same person. Template models dominated early work in speaker verification and speech 

recognition because the template model is intuitively more reasonable. However, recent work in stochastic models has demonstrated that these models are more flexible and hence allow for better modeling of the speech production process. A very popular stochastic model for modeling the speech production process is the Hidden Markov Model (HMM). HMMs are extensions to the conventional Markov models, wherein the observations are a probabilistic function of the state, i.e., the model is a doubly embedded stochastic process where the underlying stochastic process is not directly observable (it is hidden). The HMM can only be viewed through another set of stochastic processes that produce the sequence of observations. Thus, the HMM is a finite-state machine, where a probability density function p(x | s_i) is associated with each state s_i. The states are connected by a transition network, where the state transition probabilities are a_{ij} = p(s_i | s_i). A fully connected three-state HMM is depicted in figure 4.

For speech signals, another type of HMM, called a left-right model or a Bakis model, is found to be more useful. A left-right model has the property that as time increases, the state index increases (or stays the same)-- that is the system states proceed from left to right. Since the properties of a speech signal change over time in a successive manner, this model is very well suited for modeling the speech production process.

Pattern Matching :

The pattern matching process involves the comparison of a given set of input feature vectors against the speaker model for the claimed identity and computing a matching score. For the

Hidden Markov models discussed above, the matching score is the probability that a given set of feature vectors was generated by the model.