Biometry: Difference between revisions
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(Removed unnecessary copy of the slides, will add real content soon) |
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=Overview= |
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*Broad Overview on Biometric Criteria |
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*Facial recognition |
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**Use, Scenario, Features |
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**General course |
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**Approaches |
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**Summary |
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*Insights on Fingerprints |
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**Features |
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**Acquisition |
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**Recognition |
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**Demonstration |
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**Problems |
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=Short Introduction= |
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*I biometry: the statistical analysis of biological |
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observations and phenomena (Merriam-Webster |
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Online Dictionary) |
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*I biometric: a measurable, physical characteristic or |
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personal behavioral trait used to recognize the |
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identity, or verify the claimed identity of a person |
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*I Three levels: |
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**1. I know something (passwords) |
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**2. I have something (tokens) |
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**3. I am something (biometry) |
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=Classification= |
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Two diffent modes must be distinguished: |
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*Identification “Who is it?” |
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*Authentication “Is it her?” |
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=Quality indices= |
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False Rejection Rate (frr) |
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*Rate of authentication attempts from legitimate users that are rejected. |
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False Acceptance Rate (far) |
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*Rate of authentication attemps from illegitimate users (e.g. attackers) that are accepted. |
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Equal Error Rate (eer) |
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*The eer equals the far and frr when the system is configured so that far = frr. |
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Failure to Enroll Rate (fer) |
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*Rate of users that can’t enroll into the system at all. |
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=Hand geometry= |
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*Hand is placed on a reflective surface |
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*Hand is lit by some light source (e.g. bright LED) |
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*a camera and some mirrors are used to gather images of several parts of the hand from several angles |
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*the template consists of measurements extracted from the image |
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=Retina scanning= |
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*was available before iris scanning |
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*good (e.g. low) far |
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*a low intensity light source is used to scan the vascular pattern |
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=Other techniques= |
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*voice verification |
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*signature verification |
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*vein pattern scanning |
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*scent |
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*keystroke patterns |
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=Use= |
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*artifical intelligence |
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**autonomic systems should interact with human, recognize and interpret |
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**facial expressions as essential component |
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*civil authentification |
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**drivers licence, credit cards, access |
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**inconspicuous, good acceptance by users expected |
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**question: reliability, frontiers (twins) |
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*identification in delinquency (crime) |
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**cameras, automatical identification of suspects |
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**support search for missing persons |
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=Scenario= |
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*controlled environment |
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**uniform head position, cooperative users, good |
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image quality |
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*uncontrolled environment |
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**face has to be found, position, orientation, size, illumination (shadows), bakground, are variable and have to be compensated |
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**facial expression (laughing) has big influence |
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**face partially hidden, beard, hairstyle, glasses, hat can hide parts of the facial information |
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**injuries, holiday, age change the face |
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=Definition of Features= |
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*before identification |
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**face has to be found and separated from the background |
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**separating background and illumination are big problems |
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*manual definition of features |
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*calculation of features often not reliable |
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*amount of manually defined features too small to separate different people |
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*immense variety of features has to be expected |
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=Generating Templates= |
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comparison |
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*different procedures are used |
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*general: |
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**byte-comparison of two templates |
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**use of algorithms (vector operations) |
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**grade of similarity |
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**if within tolerance then identical |
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*algorithms: |
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**template matching |
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**elastical bunch graph matching |
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**geometrical features |
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**“eigenfaces” |
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=Template Matching= |
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*similarity between image and template is calculated |
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*template: for whole face or parts (eye, mouth, nose) |
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*unknown face is compared with all that were saved |
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*problems |
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**mask has to match for a lot of faces |
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**a lot of time necessary, quality depends from mask |
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=Elastic Bunch Graph Matching= |
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*Labeled Graph (LG): nodes at certain parts of face (nose, mouth,..); universal face graph |
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*Bunch Graph (BG): every node gets different features as a subgraph (specializing the LG) |
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*Facial Recognition: find face, find certain parts, calculate BG and compare graphs |
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*save useful variation |
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=Deformable Templates= |
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*analyze face with geometrical figures |
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*alignment, shape, size, but also especially the position of the elements to each other |
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*samples (templates) are adapted to concrete face |
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*notes |
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**Templates comparable with LG; has to be adapted to concrete face |
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**Extraction of edges necessary; not very reliable |
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=Recognition with geometrical features= |
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*geometrical features are extracted from face |
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**simple extraction, difficult classification |
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*possible features |
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**horizontal and vertical projection of parts (eyes,nose, . . .) |
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**position, central point, width, thickness, radius of parts of the face |
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="Eigenfaces"= |
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*beginning: amount of faces: |
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**1. mean value calculation of the sample faces |
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**2. mean value from step 1 subtracted from every sample face |
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**3. “Kovarianzmatrix” calculation of normalized images from step 2 |
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**4. mathematical procedure gives optimal base of Eigenfaces |
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*base for effizient facial recognition / data compression |
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*condition: all faces to be recognized were trained before and represented: (calculation of “Eigenfaces-Base”) |
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*recognition of an image |
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**projection into “Eigenfaces-Base” |
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**Comparison of resulting values |
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**assign to registered face (person) thats value is next to |
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*problem: classify unknown faces |
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=3D Recognition= |
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*Features: |
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**developed in israel |
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**further development at Siemens/Munich |
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**“contoured face mask” is built up |
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**superior quality compared with 2D images |
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**very fast and reliable |
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**does not care about the position of the face |
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*How it works: |
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**project coloured lines |
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**at varying heights no straight lines |
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**position of each pixel is calculated to geometric data |
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**(3D Image at precision of 0.2 x 0.2 x 0.2mm within 40 ms) |
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**image analysed by using classical recognition methods |
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**! more secure |
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**! impossible to cheat the system by placing a photo in front of the camera |
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=Summary= |
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*Evaluation and Comparison |
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**feature based approaches |
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***can handle different orientations of faces |
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***manual definition of features |
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***automatical extraction of features / classification not very reliable |
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**appearance based approaches |
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***for every view separate image of the person is necessary |
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***simple to realize, automatical feature extraction |
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=Insights on Fingerprints= |
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*Features found on fingers |
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*Acquisition of fingerprints |
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*Recognition of fingerprints |
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*Some demonstration |
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*Problems of fingerprints |
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=Classification of fingerprints= |
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In the Henry system of fingerprint classification there are five different classes of fingerprints: |
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*Right Loop |
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*Left Loop |
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*Whorl |
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*Arch |
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*Tented Arch |
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The frequency of each class seems to vary with ethnic origin: Europeans have mostly loops, Pygmies and African bush men mostly have archs and Orientals have plenty of whorls. |
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=Minutiae= |
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Different types of minutiae: |
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*endings |
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*bifurcations |
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=Devices= |
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There’s a plethora of different sensor types: |
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*capacitive |
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*optical (several principles) |
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*thermic |
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*ultrasonic |
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*pressure |
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Acquisition methodology: |
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touching vs. sweeping |
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=Typical extraction flow= |
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*fingerprint image |
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*binarization |
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*thinning |
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*minutiae extraction |
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*post processing |
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*minutiae |
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=Problems= |
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Encryption . . . Fingerprints |
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*are easy to steal |
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*can’t be changed once they are stolen |
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*are easy to fake on the most common sensor devices |
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*are usually not universally applicable (fer 5%) |
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=References= |
=References= |
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*Ashbourn, J. (2000). Biometrics: Advanced identity verification: The complete guide. London: Springer. |
*Ashbourn, J. (2000). Biometrics: Advanced identity verification: The complete guide. London: Springer. |
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*Bhanu, B., & Tan, X. (2004). Computational algorithms for fingerprint recognition. Boston / Dordrecht/ London: Kluwer Academic Publishers. |
*Bhanu, B., & Tan, X. (2004). Computational algorithms for fingerprint recognition. Boston / Dordrecht/ London: Kluwer Academic Publishers. |
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*Thalheim, L., Krissler, J., & Ziegler, P.-M. (2002). Koerperkontrolle [Body check]. In c’t 11/2002. Hannover: Heise. (English version at http: |
*Thalheim, L., Krissler, J., & Ziegler, P.-M. (2002). Koerperkontrolle [Body check]. In c’t 11/2002. Hannover: Heise. (English version at [http://www.heise.de/ct/english/02/11/114/ heise online]) |
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Also see the slides at [http://www.informatik.hu-berlin.de/~ploetz/biometry] (PDF format, 5.5 MB). |
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Revision as of 09:50, 21 February 2005
References
- Ashbourn, J. (2000). Biometrics: Advanced identity verification: The complete guide. London: Springer.
- Bhanu, B., & Tan, X. (2004). Computational algorithms for fingerprint recognition. Boston / Dordrecht/ London: Kluwer Academic Publishers.
- Thalheim, L., Krissler, J., & Ziegler, P.-M. (2002). Koerperkontrolle [Body check]. In c’t 11/2002. Hannover: Heise. (English version at heise online)
Also see the slides at [1] (PDF format, 5.5 MB).