Image credit: findbiometrics.com
What’s up with biometrics?
Each and every human being is unique and special. Feelings, knowledge, emotions, aspirations and temperament differ quite drastically from one individual to another. These characteristics of the mind are volatile, unstable and intangible. If we turn our attention to the physical attributes of a person such as face, fingerprint, iris, ear, voice, DNA pattern and the like, it’s apparent that they are not only unique and distinct, but also stable and measurable. And hence the application of biometrics in accurately identifying and authenticating the individuals or to grant access.
Biometrics refers to metrics related to human characteristics. Because they are unique and reliable, hardware sensors and softwares have been developed to,
- Authenticate the individuals
- Access control to resources
- Identify individuals within a group
Biometrics involves the application of statistics to the biological data. The degree of accuracy in pinpointing the person using biometrics far surpasses the traditional methods such as token-based identification systems (using driver’s license or passport) or the knowledge-based identification systems (using password or personal identification number).
Why only some biometric identifiers?
Among the many unique characteristics of an individual, some are preferred over the other. For an automated capture and comparison of a biological attribute, it should meet the following requirements:
- Universality: The attribute must be one that is universal and seldom lost by an accident or disease.
- Invariance of properties: They should be constant over a long period of time. The attribute should not be subject to significant differences based on age either episodic or chronic disease.
- Measurability: The properties should be suitable for capture without delay and must be easy to gather the attribute data passively.
- Singularity: Each expression of the attribute must be unique to the individual. The characteristics should have sufficient unique properties to distinguish one person from any other. Height, weight, hair and eye color are all attributes that are unique assuming a particularly precise measure, but do not offer enough points of differentiation to be useful for more than categorizing.
- Acceptance: The capturing should be possible in a way acceptable to a large percentage of the population. Excluded are particularly invasive technologies, i.e. technologies which require a part of the human body to be taken or which (apparently) impair the human body.
- Reducibility: The captured data should be capable of being reduced to a file which is easy to handle.
- Reliability and tamper-resistance: The attribute should be impractical to mask or manipulate. The process should ensure high reliability and reproducibility.
- Privacy: The process should not violate the privacy of the person.
- Comparable: Should be able to reduce the attribute to a state that makes it digitally comparable to others. The less probabilistic the matching involved, the more authoritative the identification.
- Inimitable: The attribute must be irreproducible by other means. The less reproducible the attribute, the more likely it will be authoritative.
How are biometric identifiers categorized?
Biometric identifiers are broadly categorized as physiological versus behavioral characteristics.
1. Physiological biometrics: Physiological characteristics are related to the shape and texture of the human body. Examples include, but not limited to fingerprint, palm veins, face recognition, DNA, palm print, hand geometry, iris recognition, retina and odour/scent.
2. Behavioral biometrics: Behavioral characteristics are related to the pattern of behavior of a person, including but not limited to typing rhythm, gait, and voice. Some researchers have coined the term behaviometrics to describe this class of biometrics.
Proper biometric use is very application dependent. Certain biometrics will be better than others based on the required levels of convenience and security. No single biometric will meet all the requirements of every possible application.
What’s the difference between iris recognition and retina recognition?
Iris is the coloured part of the eye. When you hear people saying Aishwarya Rai has green eyes, what they really mean is that the iris of her eyes are green.
Retina is the inner coat of the eye that’s light sensitive. Retina is like the film in the real camera on which the image forms. Retina lies behind the eye and is invisible to the naked eye. That’s why retinal scanners use IR rays to generate the image of the retina.
A retinal scanner is considered more intrusive and is also slower. For a retinal scan the subject’s eye generally has to be within 3 inches of the scanner and the subject has to focus on a point of green light that he/she would see in the scanner. The retinal scanner scans about 400 reference points that it uses for identification processes and it takes about twenty seconds.
As compared to the retinal scanner an iris scanner is a lot faster taking only about two seconds. The iris scanner can be used from a much farther distance of up to two feet and uses about 240 reference point.
So basically if the scan is taken at a very short distance and if the scan takes a little while then it is a retinal scan, and if it is done at a longer distance and is instantaneous then it is an iris scan. Now hopefully you will never make the mistake of misidentifying the two technologies.
Iris scan is faster and cheaper, but also less accurate. Retina scan though bit intrusive and slower, is basically foolproof.
How biometric systems work?
A biometric system usually operates in one of the following two modes:
1. Verification or authentication mode: The system performs a one-to-one comparison of a captured biometric with a specific template stored in a biometric database in order to verify the individual is the person they claim to be. Three steps are involved in the verification of a person. In the first step, reference models for all the users are generated and stored in the model database. In the second step, some samples are matched with reference models to generate the genuine and impostor scores and calculate the threshold. Third step is the testing step. This process may use a smart card, username or ID number (e.g. PIN) to indicate which template should be used for comparison. ‘Positive recognition’ is a common use of the verification mode, “where the aim is to prevent multiple people from using the same identity”.
2. Identification mode: The system performs a one-to-many comparison against a biometric database in an attempt to establish the identity of an unknown individual. The system will succeed in identifying the individual if the comparison of the biometric sample to a template in the database falls within a previously set threshold. Identification mode can be used either for ‘positive recognition’ (so that the user does not have to provide any information about the template to be used) or for ‘negative recognition’ of the person “where the system establishes whether the person is who she (implicitly or explicitly) denies to be”. The latter function can only be achieved through biometrics since other methods of personal recognition such as passwords, PINs or keys are ineffective.
Following block diagram depicts the logical blocks of a typical biometric system and the supporting functional units:
Image credit: Alessio Damato, wikimedia
The first time an individual uses a biometric system is called enrollment. During the enrollment, biometric information from an individual is captured and 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 enrollee.
During the enrollment phase, the template is simply stored somewhere (on a card or within a database or both). During the matching phase, 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). Selection of biometrics in any practical application depending upon the characteristic measurements and user requirements. In selecting a particular biometric, factors to consider include, performance, social acceptability, ease of circumvention and/or spoofing, robustness, population coverage, size of equipment needed and identity theft deterrence. Selection of a biometric based on user requirements considers sensor and device availability, computational time and reliability, cost, sensor size and power consumption.
What’s a multimodal biometric system?
Multimodal biometric systems use multiple sensors or biometrics to overcome the limitations of unimodal biometric systems. For instance iris recognition systems can be compromised by aging irises and finger scanning systems by worn-out or cut fingerprints. While unimodal biometric systems are limited by the integrity of their identifier, it is unlikely that several unimodal systems will suffer from identical limitations. Multimodal biometric systems can obtain sets of information from the same marker (i.e., multiple images of an iris, or scans of the same finger) or information from different biometrics (requiring fingerprint scans and, using voice recognition, a spoken pass-code).
Multimodal biometric systems can fuse these unimodal systems sequentially, simultaneously, a combination thereof, or in series, which refer to sequential, parallel, hierarchical and serial integration modes, respectively. Fusion of the biometrics information can occur at different stages of a recognition system. In case of feature level fusion, the data itself or the features extracted from multiple biometrics are fused. Matching-score level fusion consolidates the scores generated by multiple classifiers pertaining to different modalities. Finally, in case of decision level fusion the final results of multiple classifiers are combined via techniques such as majority voting. Feature level fusion is believed to be more effective than the other levels of fusion because the feature set contains richer information about the input biometric data than the matching score or the output decision of a classifier. Therefore, fusion at the feature level is expected to provide better recognition results.
Spoof attacks consist in submitting fake biometric traits to biometric systems, and are a major threat that can curtail their security. Multi-modal biometric systems are commonly believed to be intrinsically more robust to spoof attacks, but recent studies have shown that they can be evaded by spoofing even a single biometric trait.
What are few recent advances in the emerging biometrics?
In recent times, biometrics based on brain (electroencephalogram) and heart (electrocardiogram) signals have emerged. The research group at University of Kent led by Ramaswamy Palaniappan has shown that people have certain distinct brain and heart patterns that are specific for each individual. The advantage of such ‘futuristic’ technology is that it is more fraud resistant compared to conventional biometrics like fingerprints. However, such technology is generally more cumbersome and still has issues such as lower accuracy and poor reproducibility over time.
Let’s explore the performance metrics of biometrics systems in our next hangout.
Wikipedia – Biometrics
IIT Kanpur – What is Biometrics
Biometrics Institute – Types of biometrics
Scanmein – Iris vs Retina
Bioelectronix – How fingerprint optical scanner works
biometrics.gov – Biometrics technology introduction
m2sys.com – 5 ways biometric technology used in everyday life