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In order to identify a person, a security system has to compare personal characteristics with a database. A scan of a person's iris, fingerprint, face, or other distinguishing feature is created, and a series of biometric points are drawn at key locations in the scan. For example, in the case of a facial scan, biometric points might be placed at the tip of each ear lobe and in the corners of both eyes. Measurements taken between all the (possibly hundreds of) points of a scan are compiled and result in a numerical "score" (which can be quite large). This score is unique for every individual, but it can quickly and easily be compared to any compiled scores of the facial scans in the database to determine if there is a match.[ citation needed ]
For security systems using cameras, people recognition has become, in recent years, one of the most common forms of identification. The successful identification of an individual requires comparing an image of the individual to a database that contains the images of many people. However, comparing each image in its entirety, pixel by pixel, would be an extremely slow and expensive process. To solve this problem, biometrics are used. With biometrics, rather than comparing the entire image, biometric points are placed at key locations, measurements are taken between all the points, and the results are compiled into a "score." A score can be easily obtained from every image on file and stored in the database. When a new individual's image is obtained, the only requirement for successful identification is that the system needs to compile the score based on the image's biometrics and then to compare this new score to the scores in the database—an easy task for a modern computer or laptop.
The goal of a recognition system: given an image of an "unknown" person, to find a picture of the same person in a group of "known" or training images. The difficulty is ensuring that this process can be performed in real time. A biometric system identifies images or videos of people automatically. It can operate in two modes:
For the scientist, biometrics is the science of measuring physical properties of living beings and for the engineer it is the automated recognition of individuals based on their behavioural and biological characteristics.
By measuring an individual's suitable behavioural and biological characteristics in a recognition inquiry and comparing these data with the biometric reference data, which had been stored during a learning procedure, the identity of a specific user is determined.
A biometric characteristic is biological or behavioural property of an individual that can be measured and from which distinguishing, repeatable biometric features can be extracted for the purpose of automated recognition of individuals. An example is the face.
This characteristic, recorded with a capture device, can be compared with a biometric sample representation of biometric characteristics.
The biometric features are information extracted from biometric samples, which can be used for comparison with a biometric reference. Examples are characteristic measurements extracted from a photograph of a face, such as eye distance or nose size.
The aim of the extraction of biometric features from a biometric sample is to remove any information that does not contribute to biometric recognition. This enables a fast comparison, improved biometric performance, and may have privacy advantages.
Biometric characteristic | Description of the features |
---|---|
Fingerprint | Finger lines, pore structure |
Signature (dynamic) | Writing with pressure and speed differentials |
Facial geometry | Distances between specific facial features (eyes, nose, mouth) |
Iris | Iris pattern |
Retina | Eye background (pattern of retina blood vessels) |
Body geometry | Distance between specific body features |
Hand geometry | Measurement of fingers and palm |
Vein structure of hand | Vein structure of the back or palm of the hand |
Ear form | Dimensions of the visible ear |
Voice | Tone or timbre |
DNA | DNA code as the carrier of human hereditary |
Keyboard strokes | Rhythm of keyboard strokes (PC or other keyboard) |
Gait Analysis | Variations in gait style or binary gait silhouette sequences |
Touch screen (dynamic) [1] | Interaction with touchscreens and swipe gestures |
To be able to recognize a person by biometric characteristics and derived biometric features, a learning phase must first take place.
The procedure is called enrollment and comprises the creation of an enrollment data record of the biometric data subject (the person to be enrolled) and its storage in a biometric enrollment database. The enrollment data record comprises one or multiple biometric references and arbitrary non-biometric data such as a name or a personnel number.
For the purpose of recognition, the biometric data subject (the person to be recognized) presents his or her biometric characteristics to the biometric capture device, which generates a recognition biometric sample.
From this recognition biometric sample the biometric feature extraction software creates biometric features which are compared with one or multiple biometric templates from the biometric enrollment database. Due to the statistical nature of biometric samples there is generally no exact match possible. For that reason, the decision process will only assign the biometric data subject to a biometric template and confirm recognition if the comparison score exceeds an adjustable threshold.
In order to make an accurate comparison and determine if there is a match, the system requires a shape or points measurement to be compared against the information in the database. This process must be discriminating, quick to compute, concise to store, pose-independent and efficient to match.
The head shape is based in a spherical harmonics; the human head grid is mapped into a sphere and then expanded in the basics or spherical harmonics. For face recognition, the relationship between various points, such as the distance between the eyes, is compared.
For the body different kind of points are used, but, as with the head, the distances between these points are measured. Seventy-three so-called anthropometry landmarks were extracted from the scans of a database used to create this system. These are point-to-point distances. The landmarks identify key bone joint structure and are adequate to segment the body and produce anatomical reference axis systems for the key body segments and joints. Those points with separations that are pose-independent and feasibly findable in a camera’s field of view are connected by a single large bone. They form a biometric vector of twelve distances, , with , wrist to elbow, elbow to shoulder, d3 hip to knee, etc. for which the Euclidean distance is invariant across different poses. Distances such as chin-knee are avoided. All measurements are in millimeters (mm).
A computer-vision-based system will contain some errors in measurement of the landmark points. This is a complex function of the imaging system, image post-processing, and 3D calculation algorithm. For simplicity, the system does not analyze this process but instead specifies an equivalent error at the position of the landmarks, and studies the effect of this error on the recognizer.
Biometrics points are useful for making identifications with cameras systems, but they depend on the existence of a previously generated database so that distances can be compared.
Beside the most common use for people recognition in security systems, they can be used in Kinect for parental control. For example, the new data obtained is compared with previously stored data to determine if the person recognized is a minor or not.
Biometrics are body measurements and calculations related to human characteristics. Biometric authentication is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance.
Anthropometry refers to the measurement of the human individual. An early tool of physical anthropology, it has been used for identification, for the purposes of understanding human physical variation, in paleoanthropology and in various attempts to correlate physical with racial and psychological traits. Anthropometry involves the systematic measurement of the physical properties of the human body, primarily dimensional descriptors of body size and shape. Since commonly used methods and approaches in analysing living standards were not helpful enough, the anthropometric history became very useful for historians in answering questions that interested them.
Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance. The discriminating powers of all biometric technologies depend on the amount of entropy they are able to encode and use in matching. Iris recognition is exceptional in this regard, enabling the avoidance of "collisions" even in cross-comparisons across massive populations. Its major limitation is that image acquisition from distances greater than a meter or two, or without cooperation, can be very difficult. However, the technology is in development and iris recognition can be accomplished from even up to 10 meters away or in a live camera feed.
A facial recognition system is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image.
Speaker recognition is the identification of a person from characteristics of voices. It is used to answer the question "Who is speaking?" The term voice recognition can refer to speaker recognition or speech recognition. Speaker verification contrasts with identification, and speaker recognition differs from speaker diarisation.
The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.
Automatic identification and data capture (AIDC) refers to the methods of automatically identifying objects, collecting data about them, and entering them directly into computer systems, without human involvement. Technologies typically considered as part of AIDC include QR codes, bar codes, radio frequency identification (RFID), biometrics, magnetic stripes, optical character recognition (OCR), smart cards, and voice recognition. AIDC is also commonly referred to as "Automatic Identification", "Auto-ID" and "Automatic Data Capture".
In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient.
Living in the intersection of cryptography and psychology, password psychology is the study of what makes passwords or cryptographic keys easy to remember or guess.
A card reader is a data input device that reads data from a card-shaped storage medium and provides the data to a computer. Card readers can acquire data from a card via a number of methods, including: optical scanning of printed text or barcodes or holes on punched cards, electrical signals from connections made or interrupted by a card's punched holes or embedded circuitry, or electronic devices that can read plastic cards embedded with either a magnetic strip, computer chip, RFID chip, or another storage medium.
Some schools use biometric data such as fingerprints and facial recognition to identify students. This may be for daily transactions in the library or canteen or for monitoring absenteeism and behavior control. In 2002, Privacy International raised concerns that tens of thousands of UK school children were being fingerprinted by schools, often without the knowledge or consent of their parents. The supplier, Micro Librarian Systems, which uses technology similar to that used in prisons and the military, estimated that 350 schools throughout Britain were using such systems. In 2007, it was estimated that 3,500 schools are using such systems. Some schools in Belgium and the US have followed suit. Concerns have been raised by a number of groups, who suggest the harms far outweigh any putative benefits.
BioAPI is a key part of the International Standards that support systems that perform biometric enrollment and verification. It defines interfaces between modules that enable software from multiple vendors to be integrated together to provide a biometrics application within a system, or between one or more systems using a defined Biometric Interworking Protocol (BIP) – see below.
Private biometrics is a form of encrypted biometrics, also called privacy-preserving biometric authentication methods, in which the biometric payload is a one-way, homomorphically encrypted feature vector that is 0.05% the size of the original biometric template and can be searched with full accuracy, speed and privacy. The feature vector's homomorphic encryption allows search and match to be conducted in polynomial time on an encrypted dataset and the search result is returned as an encrypted match. One or more computing devices may use an encrypted feature vector to verify an individual person or identify an individual in a datastore without storing, sending or receiving plaintext biometric data within or between computing devices or any other entity. The purpose of private biometrics is to allow a person to be identified or authenticated while guaranteeing individual privacy and fundamental human rights by only operating on biometric data in the encrypted space. Some private biometrics including fingerprint authentication methods, face authentication methods, and identity-matching algorithms according to bodily features. Private biometrics are constantly evolving based on the changing nature of privacy needs, identity theft, and biotechnology.
Body identification is a subfield of forensic science that uses a variety of scientific and non-scientific methods to identify a body. Forensic purposes are served by rigorous scientific forensic identification techniques, but these are generally preceded by formal identification. This involves requesting a family member or friend of the victim to visually identify the body.
This is a software system for forensic comparison of handwriting. It was developed at CEDAR, the Center of Excellence for Document Analysis and Recognition at the University at Buffalo. CEDAR-FOX has capabilities for interaction with the questioned document examiner to go through processing steps such as extracting regions of interest from a scanned document, determining lines and words of text, recognize textual elements. The final goal is to compare two samples of writing to determine the log-likelihood ratio under the prosecution and defense hypotheses. It can also be used to compare signature samples. The software, which is protected by a United States Patent can be licensed from Cedartech, Inc.
Soft Biometrics traits are physical, behavioural or adhered human characteristics, classifiable in pre–defined human compliant categories. These categories are, unlike in the classical biometric case, established and time–proven by humans with the aim of differentiating individuals. In other words the soft biometric traits instances are created in a natural way, used by humans to distinguish their peers.
Vein matching, also called vascular technology, is a technique of biometric identification through the analysis of the patterns of blood vessels visible from the surface of the skin. Though used by the Federal Bureau of Investigation and the Central Intelligence Agency, this method of identification is still in development and has not yet been universally adopted by crime labs as it is not considered as reliable as more established techniques, such as fingerprinting. However, it can be used in conjunction with existing forensic data in support of a conclusion.
A whole new range of techniques has been developed to identify people since the 1960s from the measurement and analysis of parts of their bodies to DNA profiles. Forms of identification are used to ensure that citizens are eligible for rights to benefits and to vote without fear of impersonation while private individuals have used seals and signatures for centuries to lay claim to real and personal estate. Generally, the amount of proof of identity that is required to gain access to something is proportionate to the value of what is being sought. It is estimated that only 4% of online transactions use methods other than simple passwords. Security of systems resources generally follows a three-step process of identification, authentication and authorization. Today, a high level of trust is as critical to eCommerce transactions as it is to traditional face-to-face transactions.
Biometrics refers to the automated recognition of individuals based on their biological and behavioral characteristics, not to be confused with statistical biometrics; which is used to analyse data in the biological sciences. Biometrics for the purposes of identification may involve DNA matching, facial recognition, fingerprints, retina and iris scanning, voice analysis, handwriting, gait, and even body odor.
A biometric device is a security identification and authentication device. Such devices use automated methods of verifying or recognising the identity of a living person based on a physiological or behavioral characteristic. These characteristics include fingerprints, facial images, iris and voice recognition.
Biometric selection body parts
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