Biometric Device Authentication

Published on June 2016 | Categories: Types, School Work | Downloads: 29 | Comments: 0 | Views: 301
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it contains biometric authentication of 4 types1)fingerprint scan2)iris recognition3)face recognition4)voice recognition




Biometrics (derived from the Greek words bios = "life" and metron = "measure") is the study and development of automated methods for the identification and authentication of individuals based on each person's unique physical and behavioural traits. Examples of unique physical characteristics include fingerprints, eye retinas and irises, facial patterns, hand measurements, and DNA sequences (DNA "fingerprints"). Thus the devices that help in recognizing an individual on the basis of his fingerprint, iris, facial or voice are know as BIOMETRIC DEVICES. Examples of such devices are Finger print scanner, camera, microphone together with their respective software.

While biometrics did not show up in practice in Western cultures until the late nineteenth century, it was being used in China by at least the fourteenth century. Explorer and writer Joao de Barros recorded that Chinese merchants stamped children s palm prints and footprints on paper with ink, as a way to distinguish young children from one another. In the West, identification relied heavily on "photographic memory" until Alphonse Bertillon, a French police desk clerk and anthropologist, developed the "anthropometric" a system (later known as Bertillonage) in 1883. It was the first precise, scientific system widely used to identify criminals. It turned biometrics into a field of study. After that, Western police forces turned to fingerprinting Until recently, fingerprinting was used mainly for forensics and criminal identification. With the development of biometrics technologies, silicon-based sensors that produce digital images of the fingerprint have replaced printer s ink, and this new approach can be used as a means to secure access to a place (such as an office) or device (such as a computer). Moreover, the scope of biometrics has been expanded to include many different methods involving the measurement of various physical and behavioural traits.



A fingerprint in its narrow sense is an impression left by the friction ridges of a human finger. In a wider use of the term, fingerprints are the traces of an impression from the friction ridges of any part of a human hand. A print from the foot can also leave an impression of friction ridges. A friction ridge is a raised portion of the epidermis on the fingers and toes (digits), the palm of the hand or the sole of the foot, consisting of one or more connected ridge units of friction ridge skin. These are sometimes known as "epidermal ridges . Impressions of fingerprints may be left behind on a surface by the natural secretions of sweat from the endocrine glands that are present in friction ridge skin

Human fingerprints have been discovered on a large number of archaeological artifacts and historical items In 1684, the English plant morphologist, Nehemiah Grew, published the first scientific paper reporting his systematic study on the ridge, furrow, and pore structure In 1788, a detailed description of the anatomical formations of fingerprints was made by Mayer. In 1823, Purkinji proposed the first fingerprint classification, which classified into nine categories Sir Francis Galton introduced the minute features for fingerprint matching in late 19th century

Fingerprint Scanning Fingerprint Matching Identification

Fingerprint image acquisition is considered to be the most critical step in an automated fingerprint authentication system, as it determines the final fingerprint image quality, which has a drastic effect on the overall system performance. There are different types of fingerprint readers on the market, but the basic idea behind each is to measure the physical difference between ridges and valleys. All the proposed methods can be grouped into two major families: solid-state fingerprint readers and optical fingerprint readers. The procedure for capturing a fingerprint using a sensor consists of rolling or touching with the finger onto a sensing area, which according to the physical principle in use (optical, ultrasonic, capacitive or thermal) captures the difference between valleys and ridges. When a finger touches or rolls onto a surface, the elastic skin deforms. The quantity and direction of the pressure applied by the user, the skin conditions and the projection of an irregular 3D object (the finger) onto a 2D flat plane introduce distortions, noise and inconsistencies in the captured fingerprint image. These problems result in inconsistent, irreproducible and non-uniform irregularities in the image. During each acquisition, therefore, the results of the imaging are different and uncontrollable. The representation of the same fingerprint changes every time the finger is placed on the sensor plate, increasing the complexity of any attempt to match fingerprints, impairing the system performance and consequently, limiting the widespread use of this biometric technology.


Fingerprints don t change over time Widely believed fingerprints are unique


Surgery to alter or remove prints Finger Decapitation Gummy fingers Corruption of the database

Measure physical properties of a live finger (pulse

A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems.

Face recognition system [TrueFace Engine by Miros]

Face recognition is relatively new concept. Developed in 1960s, the semi-automated system for face recognition required the administrator to locate features(eyes, nose, ears and mouth) on the photographs before it calculated distances and ratios to a common reference point, which are then compared to reference data. In the 1970s, Goldstein, Harmon, and Lesk used 21 specified subjective markers such as hair color and lip thickness to automate the recognition. The problem with both of these early solution was that the measurement and locations were manually computed. In 1988, Kirby and Sirovich applied principle component analysis, a standard linear algebra technique, to the face recognition problem. This was considered somewhat of a milestone as it showed less than one hundred values were required to accurately code a suitably aligned and normalized face image

>>Facial recognition requires 2 steps: Facial Detection Facial Recognition >>Typical Facial Recognition technology automates the recognition of faces using one of two 2 modeling approaches: Face appearance 2D Eigen faces 3D Morph able Model Face geometry 3D Expression Invariant Recognition

1) 2D Eigen face 2) 3D Face Recognition --3D Expression Invariant Recognition --3D Morph able Model

‡ Decompose face images into a small set of characteristic feature images. ‡ A new face is compared to these stored images. ‡ A match is found if the new faces is close to one of these images.


3D Expression Invariant Recognition
‡ Treats face as a deformable object. ‡ 3D system maps a face. ‡ Captures facial geometry in canonical form. ‡ Can be compared to other canonical forms.

‡ Cr ate a face model images. from ‡ Synt etic facial images are created to add to training set.

Typical Face Authentication Workflow

‡ Strengths:
± Database can be built from driver s license records, visas, etc. ± Few people object to having their photo taken.

‡ Weaknesses:
± No real scientific validation

‡ Attacks:
± Surgery ± Facial Hair ± Hats

‡ Defenses:
± Scanning stations with mandated poses

Iris is the area of the eye where the pigmented or colored circle, usually brown, black, rings the dark pupil of the eye.

 Iris Localization  Iris Normalization  Image Enhancement

Both the inner boundary and the outer boundary of a typical iris can be taken as circles. But the two circles are usually not co-centric. Compared with the other part of the eye, the pupil is much darker. We detect the inner boundary between the pupil and the iris. The outer boundary of the iris is more difficult to detect because of the low contrast between the two sides of the boundary. We detect the outer boundary by maximizing changes of the perimeter- normalized along the circle. The technique is found to be efficient and effective.

 The size of the pupil may change due to the variation of the
illumination and the associated elastic deformations in the iris texture may interface with the results of pattern matching. For the purpose of accurate texture analysis, it is necessary to compensate this deformation. Since both the inner and outer boundaries of the iris have been detected, it is easy to map the iris ring to a rectangular block of texture of a fixed size.

The original image has low contrast and may have non-uniform illumination caused by the position of the light source. These may impair the result of the texture analysis. We enhance the iris image by reducing the effect of non-uniform illumination.

‡ Strengths:
± 300+ characteristics; 200 required for match

‡ Weaknesses:
± ± ± ± Fear Discomfort Algorithms may not work on all individuals No large databases

‡ Attacks:
± Surgery

± Defenses:

Voice recognition systems look for voice pattern matches and should not be confused with speech recognition, which interprets what is said. Voice recognition systems have the advantage of being user friendly, but such things as background noise and changes to the voice due to colds, sinus congestion, anxiety, etc. can result in false negatives. Voice recognition systems also require a great deal of disk space, and entering an individual into the system is time consuming.

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