Ohh!!, Today we had Guest Lecture on BIOMETRICS by the Professor From IIT Roorkee.
They gave us a brief introduction on BIOMETRICS with slide presentation the Devices and the Technology us in Biometrics.
Biometrics consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. In computer science, in particular, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance.
Biometric characteristics can be divided in two main classes:-
• Physiological are related to the shape of the body. Examples include, but are not limited to fingerprint, face recognition, DNA, Palm print, hand geometry, iris recognition, which has largely replaced retina, and odour/scent.
• Behavioral are related to the behavior of a person. Examples include, but are not limited to typing rhythm, gait, and voice. Some researchers have coined the term behaviometrics for this class of biometrics.
Strictly speaking, voice is also a physiological trait because every person has a different vocal tract, but voice recognition is mainly based on the study of the way a person speaks, commonly classified as behavioral.
The basic block diagram of a biometric system
It is possible to understand if a human characteristic can be used for biometrics in terms of the following parameters:
• Universality – each person should have the characteristic.
• Uniqueness – is how well the biometric separates individuals from another.
• Permanence – measures how well a biometric resists aging and other variance over time.
• Collectability – ease of acquisition for measurement.
• Performance – accuracy, speed, and robustness of technology used.
• Acceptability – degree of approval of a technology.
• Circumvention – ease of use of a substitute.
A biometric system can operate in the following two
• Verification – A one to one comparison of a captured biometric with a stored template to verify that the individual is who he claims to be. Can be done in conjunction with a smart card, username or ID number.
• Identification – A one to many comparison of the captured biometric against a biometric database in attempt to identify an unknown individual. The identification only succeeds in identifying the individual if the comparison of the biometric sample to a template in the database falls within a previously set threshold.
The first time an individual uses a biometric system is called an enrollment. During the enrollment, biometric information from an individual is stored. In subsequent uses, biometric information is detected and compared with the information stored at the time of enrollment. Note that it is crucial that storage and retrieval of such systems themselves be secure if the biometric system is to be robust. The first block (sensor) is the interface between the real world and the system; it has to acquire all the necessary data. Most of the times it is an image acquisition system, but it can change according to the characteristics desired. The second block performs all the necessary pre-processing: it has to remove artifacts from the sensor, to enhance the input (e.g. removing background noise), to use some kind of normalization, etc. In the third block necessary features are extracted. This step is an important step as the correct features need to be extracted in the optimal way. A vector of numbers or an image with particular properties is used to create a template. A template is a synthesis of the relevant characteristics extracted from the source. Elements of the biometric measurement that are not used in the comparison algorithm are discarded in the template to reduce the filesize and to protect the identity of the enrolled.
If enrollment is being performed, the template is simply stored somewhere (on a card or within a database or both). If a matching phase is being performed, the obtained template is passed to a matcher that compares it with other existing templates, estimating the distance between them using any algorithm (e.g. Hamming distance). The matching program will analyze the template with the input. This will then be output for any specified use or purpose (e.g. entrance in a restricted area).
Performance
The following are used as performance metrics for biometric systems:[3] just a trial
• false accept rate or false match rate (FAR or FMR) – the probability that the system incorrectly matches the input pattern to a non-matching template in the database. It measures the percent of invalid inputs which are incorrectly accepted.
• false reject rate or false non-match rate (FRR or FNMR) – the probability that the system fails to detect a match between the input pattern and a matching template in the database. It measures the percent of valid inputs which are incorrectly rejected.
• Receiver operating characteristic or relative operating characteristic (ROC) – The ROC plot is a visual characterization of the trade-off between the FAR and the FRR. In general, the matching algorithm performs a decision based on a threshold which determines how close to a template the input needs to be for it to be considered a match. If the threshold is reduced, there will be less false non-matches but more false accepts. Correspondingly, a higher threshold will reduce the FAR but increase the FRR. A common variation is the Detection error trade-off (DET), which is obtained using normal deviate scales on both axes. This more linear graph illuminates the differences for higher performances (rarer errors).
• equal error rate or crossover error rate (EER or CER) – the rate at which both accept and reject errors are equal. The value of the EER can be easily obtained from the ROC curve. The EER is a quick way to compare the accuracy of devices with different ROC curves. In general, the device with the lowest EER is most accurate.
• failure to enroll rate (FTE or FER) – the rate at which attempts to create a template from an input is unsuccessful. This is most commonly caused by low quality inputs.
• failure to capture rate (FTC) – Within automatic systems, the probability that the system fails to detect a biometric input when presented correctly.
• template capacity – the maximum number of sets of data which can be stored in the system.
Iris recognition :-
is a method of biometric authentication that uses pattern-recognition techniques based on high-resolution images of the irides of an individual's eyes.
Not to be confused with another, less prevalent, ocular-based technology, retina scanning, iris recognition uses camera technology, with subtle infrared illumination reducing specular reflection from the convex cornea, to create images of the detail-rich, intricate structures of the iris. Converted into digital templates, these images provide mathematical representations of the iris that yield unambiguous positive identification of an individual.
The melanin, also known as chromophore, mainly consists of two distinct heterogeneous macromolecules, called eumelanin (brown–black) and pheomelanin (yellow–reddish). NIR imaging is not sensitive to these chromophores, and as a result they do not appear in the captured images. In contrast, visible wavelength (VW) imaging keeps the related chromophore information and, compared to NIR, provides rich sources of information mainly coded as shape patterns in iris. Hosseini et al. provide a comparison between these two imaging modalities and fused the results to boost the recognition rate. An alternative feature extraction method to encode VW iris images was also introduced, which is highly robust to reflectivity terms in iris. Such fusion results are seemed to be alternative approach for multi-modal biometric systems which intend to reach high accuracies of recognition in large databanks
By:- Atul Purohit
They gave us a brief introduction on BIOMETRICS with slide presentation the Devices and the Technology us in Biometrics.
Biometrics consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. In computer science, in particular, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance.
Biometric characteristics can be divided in two main classes:-
• Physiological are related to the shape of the body. Examples include, but are not limited to fingerprint, face recognition, DNA, Palm print, hand geometry, iris recognition, which has largely replaced retina, and odour/scent.
• Behavioral are related to the behavior of a person. Examples include, but are not limited to typing rhythm, gait, and voice. Some researchers have coined the term behaviometrics for this class of biometrics.
Strictly speaking, voice is also a physiological trait because every person has a different vocal tract, but voice recognition is mainly based on the study of the way a person speaks, commonly classified as behavioral.
The basic block diagram of a biometric system
It is possible to understand if a human characteristic can be used for biometrics in terms of the following parameters:
• Universality – each person should have the characteristic.
• Uniqueness – is how well the biometric separates individuals from another.
• Permanence – measures how well a biometric resists aging and other variance over time.
• Collectability – ease of acquisition for measurement.
• Performance – accuracy, speed, and robustness of technology used.
• Acceptability – degree of approval of a technology.
• Circumvention – ease of use of a substitute.
A biometric system can operate in the following two
• Verification – A one to one comparison of a captured biometric with a stored template to verify that the individual is who he claims to be. Can be done in conjunction with a smart card, username or ID number.
• Identification – A one to many comparison of the captured biometric against a biometric database in attempt to identify an unknown individual. The identification only succeeds in identifying the individual if the comparison of the biometric sample to a template in the database falls within a previously set threshold.
The first time an individual uses a biometric system is called an enrollment. During the enrollment, biometric information from an individual is stored. In subsequent uses, biometric information is detected and compared with the information stored at the time of enrollment. Note that it is crucial that storage and retrieval of such systems themselves be secure if the biometric system is to be robust. The first block (sensor) is the interface between the real world and the system; it has to acquire all the necessary data. Most of the times it is an image acquisition system, but it can change according to the characteristics desired. The second block performs all the necessary pre-processing: it has to remove artifacts from the sensor, to enhance the input (e.g. removing background noise), to use some kind of normalization, etc. In the third block necessary features are extracted. This step is an important step as the correct features need to be extracted in the optimal way. A vector of numbers or an image with particular properties is used to create a template. A template is a synthesis of the relevant characteristics extracted from the source. Elements of the biometric measurement that are not used in the comparison algorithm are discarded in the template to reduce the filesize and to protect the identity of the enrolled.
If enrollment is being performed, the template is simply stored somewhere (on a card or within a database or both). If a matching phase is being performed, the obtained template is passed to a matcher that compares it with other existing templates, estimating the distance between them using any algorithm (e.g. Hamming distance). The matching program will analyze the template with the input. This will then be output for any specified use or purpose (e.g. entrance in a restricted area).
Performance
The following are used as performance metrics for biometric systems:[3] just a trial
• false accept rate or false match rate (FAR or FMR) – the probability that the system incorrectly matches the input pattern to a non-matching template in the database. It measures the percent of invalid inputs which are incorrectly accepted.
• false reject rate or false non-match rate (FRR or FNMR) – the probability that the system fails to detect a match between the input pattern and a matching template in the database. It measures the percent of valid inputs which are incorrectly rejected.
• Receiver operating characteristic or relative operating characteristic (ROC) – The ROC plot is a visual characterization of the trade-off between the FAR and the FRR. In general, the matching algorithm performs a decision based on a threshold which determines how close to a template the input needs to be for it to be considered a match. If the threshold is reduced, there will be less false non-matches but more false accepts. Correspondingly, a higher threshold will reduce the FAR but increase the FRR. A common variation is the Detection error trade-off (DET), which is obtained using normal deviate scales on both axes. This more linear graph illuminates the differences for higher performances (rarer errors).
• equal error rate or crossover error rate (EER or CER) – the rate at which both accept and reject errors are equal. The value of the EER can be easily obtained from the ROC curve. The EER is a quick way to compare the accuracy of devices with different ROC curves. In general, the device with the lowest EER is most accurate.
• failure to enroll rate (FTE or FER) – the rate at which attempts to create a template from an input is unsuccessful. This is most commonly caused by low quality inputs.
• failure to capture rate (FTC) – Within automatic systems, the probability that the system fails to detect a biometric input when presented correctly.
• template capacity – the maximum number of sets of data which can be stored in the system.
Iris recognition :-
is a method of biometric authentication that uses pattern-recognition techniques based on high-resolution images of the irides of an individual's eyes.
Not to be confused with another, less prevalent, ocular-based technology, retina scanning, iris recognition uses camera technology, with subtle infrared illumination reducing specular reflection from the convex cornea, to create images of the detail-rich, intricate structures of the iris. Converted into digital templates, these images provide mathematical representations of the iris that yield unambiguous positive identification of an individual.
Iris recognition efficacy is rarely impeded by glasses or contact lenses. Iris technology has the smallest outlier (those who cannot use/enroll) group of all biometric technologies. Because of its speed of comparison, iris recognition is the only biometric technology well-suited for one-to-many identification. A key advantage of iris recognition is its stability, or template longevity, as, barring trauma, a single enrollment can last a lifetime.
Breakthrough work to create the iris-recognition algorithms required for image acquisition and one-to-many matching was pioneered by John G. Daugman, Ph.D, OBE (University of Cambridge Computer Laboratory). These were utilized to effectively debut commercialization of the technology in conjunction with an early version of the IrisAccess system designed and manufactured by Korea's LG Electronics. Daugman's algorithms are the basis of almost all currently (as of 2006) commercially deployed iris-recognition systems. (In tests where the matching thresholds are—for better comparability—changed from their default settings to allow a false-accept rate in the region of 10−3 to 10−4 [1], the Iris Code false-reject rates are comparable to the most accurate single-finger fingerprint matchers.
Visible Wavelength (VW) vs Near Infrared (NIR) Imaging:-
The majority of iris recognition benchmarks are implemented in Near Infrared (NIR) imaging by emitting 750 nm wavelength light source. This is done to avoid light reflections from cornea in iris which makes the captured images very noisy. Such images are challenging for feature extraction procedures and consequently hard to recognize at the identification step. Although, NIR imaging provides good quality images, it loses pigment melanin information, which is a rich source of information for iris recognition.
The majority of iris recognition benchmarks are implemented in Near Infrared (NIR) imaging by emitting 750 nm wavelength light source. This is done to avoid light reflections from cornea in iris which makes the captured images very noisy. Such images are challenging for feature extraction procedures and consequently hard to recognize at the identification step. Although, NIR imaging provides good quality images, it loses pigment melanin information, which is a rich source of information for iris recognition.
Visible Wavelength Iris Image Near Infrared (NIR) version
The melanin, also known as chromophore, mainly consists of two distinct heterogeneous macromolecules, called eumelanin (brown–black) and pheomelanin (yellow–reddish). NIR imaging is not sensitive to these chromophores, and as a result they do not appear in the captured images. In contrast, visible wavelength (VW) imaging keeps the related chromophore information and, compared to NIR, provides rich sources of information mainly coded as shape patterns in iris. Hosseini et al. provide a comparison between these two imaging modalities and fused the results to boost the recognition rate. An alternative feature extraction method to encode VW iris images was also introduced, which is highly robust to reflectivity terms in iris. Such fusion results are seemed to be alternative approach for multi-modal biometric systems which intend to reach high accuracies of recognition in large databanks
By:- Atul Purohit
