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There is a wide variety of biometric scanners and integration techniques. Below we have broken down the different types of biometric scanners. If you have any questions contact us at (916) 550-4386
Biometric Retina Scanner
The human retina is a thin tissue made up of neural cells that is based in the posterior portion of the eye. Due to the complex structure of the capillaries that supply the retina with blood, every person’s retina is unique. The network of arteries in the retina is not entirely genetically determined and so even identical twins usually do not share a similar pattern.
Although retinal patterns may be altered in cases of glaucoma, retinal degenerative disorders or diabetes, the retina typically remains unchanged from birth until death. Because of its unique and unchanging nature, the retina seems to be the most precise and reliable biometric, in addition to DNA. Advocates of retinal scanning have figured it is so accurate that its error rates are estimated to be only one inside a million.
A biometric identifier termed as a retinal scan is used to map the patterns of a person’s retina. The bloodstream within the retina absorb light more readily than the surrounding tissue and are easily identified with appropriate lighting. The system performs a retinal scan by casting an unperceived beam of low-energy infrared light in to a person’s eye as they look through the scanner’s eyepiece. This laser beam traces a standardized path about the retina. Because retinal blood vessels are more absorbent on this light than the rest of the eye, the volume of reflection varies during the scan. The pattern of variations is transformed into computer code and held in a database.
Biometric Iris Scanner
Iris recognition is definitely an automated method of biometric identification that utilizes mathematical pattern-recognition techniques on video images of the irides of an individual’s eyes, whose complex random patterns are unique and can be seen from some distance.
Never to be confused with less prevalent, ocular-based technology, iris recognition, retina scanning uses camera technology with subtle infrared illumination to acquire images of the detail-rich, intricate structures in the iris. Digital templates encoded from these patterns by mathematical and statistical algorithms enable the identification of an individual or someone pretending to be that individual. Custom databases of templates are searched by matcher engines at speeds measured from the millions of templates per second per CPU, sufficient reason for infinitesimally small false match rates.
Many millions of persons in several countries around the world have been enrolled in a iris recognition system, for convenience purposes such as passport free automated border crossings, and some national ID systems determined by this technology are being deployed. An integral advantage of iris recognition, besides its speed of matching as well as extreme resistance to false matches, will be the stability of the iris as a possible internal, protected, yet externally visible organ with the eye.
Biometric Fingerprint Scanner
Finger Scanning, also known as fingerprint scanning, which is the process of electronically obtaining and storing human fingerprints. Digital image obtained by such scanning is termed a finger image. Some texts say, the terms fingerprinting and fingerprint are widely used, but technically, these terms reference traditional paper and ink processes and images.
Finger scanning is really a biometric process, because it requires the automated capture, analysis, and comparison of the specific characteristic of the human body. There are a variety of different ways in which an instrument can bring out the details in the pattern of raised areas (called ridges) and branches (called bifurcations) in a human finger image. The most frequent methods are thermal, optical, and tactile. They work using heat emission analysis, visible light analysis, and pressure analysis, as applicable.
Biometric finger scanning offers improvements over ink-and-paper imaging. A whole set of finger scans for an individual (10 images, including those of the thumbs) can be distributed, easily copied, and transmitted over computer networks. Also, computers can quickly analyze a finger scan and compare it with thousands of other finger scans, along with with fingerprints obtained by traditional means and after that digitally photographed and stored. This greatly accelerates the process of searching finger image records in criminal investigations.
Biometric Facial Recognition
Some facial recognition algorithms identify facial features by extracting landmarks, or features, from a graphic of the subject’s face. By way of example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These traits are then used to search for other images with matching attributes. Other algorithms normalize a file of face images and then compress the face data, only saving your data in the image that is ideal for face recognition. The probe image is then compared with the face data. One of the oldest successful systems is based on template matching techniques applied to a set of salient facial features, providing some type of compressed face representation.
Recognition algorithms should be divided into 2 main approaches, geometric, which examines distinguishing features, or photometric, the statistical approach that distills a picture into values and compares the values with templates to eliminate variances.
Popular recognition algorithms include Linear Discriminant Analysis, Principal Component Analysis using eigenfaces, Elastic Bunch Graph Matching with all the Fisherface algorithm, the Multilinear Subspace Learning using tensor representation, the Hidden Markov model, and also the neuronal motivated dynamic link matching.
The newest wave, claimed to achieve improved accuracies, is a three dimensional face recognition. This system uses 3D sensors to capture information about the shape of a face. This data is then used to identify distinctive features at first glance of a face, such as the contour from the nose, eye sockets, and chin.
One benefit from 3D facial recognition is that it is not affected by changes in lighting like other techniques. It may also identify a face from the range of different viewing angles.
Three-dimensional data points coming from a face vastly improve the precision of facial recognition. 3D research is enhanced with the development of sophisticated sensors which do a better job of capturing 3D face imagery. These sensors work by projecting structured light to the face. Up to a dozen or higher of these image sensors may be placed on the same CMOS chip-each sensor captures a different part of the spectrum. Even a perfect 3D matching technique might be sensitive to expressions.
Skin texture analysis
Another trend uses the visual information the skin, as captured in standard digital or scanned images. This technique is called skin texture analysis, turns the unique patterns, lines, and spots apparent within a person’s skin into a mathematical space. Tests demonstrate that with the addition of skin texture analysis, performance in recognizing faces can increase 20 to 25 %.
Biometric Voice Recognition
Speaker recognition will be the identification of the person who is speaking by characteristics of their voices (voice biometrics), also called voice recognition.
There is a difference between speaker recognition (recognizing that’s speaking) and speech recognition (recognizing precisely what is being said). These two terms are generally confused, and “voice recognition” can be used for either. In addition, there is a difference between the act of authentication (commonly referred to as speaker verification or speaker authentication) and identification. Finally, there exists a difference between speaker recognition (recognizing who is speaking) and speaker diarization (recognizing if the same speaker is speaking).
Recognizing the speaker can simplify the job of translating speech in systems which have been trained on specific person’s voices or quite a few to authenticate or verify the identity of a speaker as part of a security process.
Speaker recognition features a history dating back some 40 years and uses the acoustic top features of speech that have been found to differ between individuals. The acoustic patterns reflect both anatomy (e.g., decoration of the throat and mouth) and learned behavioral patterns (e.g., voice pitch, speaking style). Speaker verification has earned the speaker recognition in its classification like a “behavioral biometric”.
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