Biometrics
Biometrics are body measurements and calculations related to human characteristics and features. 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.
Biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals. Biometric identifiers are often categorized as physiological characteristics which are related to the shape of the body. Examples include, but are not limited to fingerprint, palm veins, face recognition, DNA, palm print, hand geometry, iris recognition, retina, odor/scent, voice, shape of ears and gait. Behavioral characteristics are related to the pattern of behavior of a person, including but not limited to mouse movement, typing rhythm, gait, signature, voice, and behavioral profiling. Some researchers have coined the term behaviometrics to describe the latter class of biometrics.
More traditional means of access control include token-based identification systems, such as a driver's license or passport, and knowledge-based identification systems, such as a password or personal identification number. Since biometric identifiers are unique to individuals, they are more reliable in verifying identity than token and knowledge-based methods; however, the collection of biometric identifiers raises privacy concerns.
Biometric functionality
Many different aspects of human physiology, chemistry or behavior can be used for biometric authentication. The selection of a particular biometric for use in a specific application involves a weighting of several factors. Jain et al. identified seven such factors to be used when assessing the suitability of any trait for use in biometric authentication. Biometric authentication is based upon biometric recognition which is an advanced method of recognizing biological and behavioural characteristics of an Individual.- Universality means that every person using a system should possess the trait.
- Uniqueness means the trait should be sufficiently different for individuals in the relevant population such that they can be distinguished from one another.
- Permanence relates to the manner in which a trait varies over time. More specifically, a trait with good permanence will be reasonably invariant over time with respect to the specific matching algorithm.
- Measurability relates to the ease of acquisition or measurement of the trait. In addition, acquired data should be in a form that permits subsequent processing and extraction of the relevant feature sets.
- Performance relates to the accuracy, speed, and robustness of technology used.
- Acceptability relates to how well individuals in the relevant population accept the technology such that they are willing to have their biometric trait captured and assessed.
- Circumvention relates to the ease with which a trait might be imitated using an artifact or substitute.
The block diagram illustrates the two basic modes of a biometric system. First, in verification 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. The third step is the testing step. This process may use a smart card, username, or ID number 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".
Second, in 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 or for negative recognition of the person "where the system establishes whether the person is who she 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.
The first time an individual uses a biometric system is called enrollment. During 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 is the interface between the real world and the system; it has to acquire all the necessary data. Most of the time 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, 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 an 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 file size and to protect the identity of the enrollee. However, depending on the scope of the biometric system, original biometric image sources may be retained, such as the PIV-cards used in the Federal Information Processing Standard Personal Identity Verification of Federal Employees and Contractors.
During the enrollment phase, the template is simply stored somewhere. 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. The matching program will analyze the template with the input. This will then be output for a specified use or purpose, though it is a fear that the use of biometric data may face mission creep.
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. The selection of a biometric is based on user requirements and considers sensor and device availability, computational time and reliability, cost, sensor size, and power consumption.
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 electronic fingerprint recognition can be worsened 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 or information from different biometrics.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.
One such proposed system of Multimodal Biometric Cryptosystem Involving the Face, Fingerprint, and Palm Vein by Prasanalakshmi The Cryptosystem Integration combines biometrics with cryptography, where the palm vein acts as a cryptographic key, offering a high level of security since palm veins are unique and difficult to forge. The Fingerprint Involves minutiae extraction and matching techniques. Steps include image enhancement, binarization, ROI extraction, and minutiae thinning. The Face system uses class-based scatter matrices to calculate features for recognition, and the Palm Vein acts as an unbreakable cryptographic key, ensuring only the correct user can access the system. The cancelable Biometrics concept allows biometric traits to be altered slightly to ensure privacy and avoid theft. If compromised, new variations of biometric data can be issued.
The Encryption fingerprint template is encrypted using the palm vein key via XOR operations. This encrypted Fingerprint is hidden within the face image using steganographic techniques. Enrollment and Verification for the Biometric data are captured, encrypted, and embedded into a face image. The system extracts the biometric data and compares it with stored values for Verification. The system was tested with fingerprint databases, achieving 75% verification accuracy at an equal error rate of 25% and processing time approximately 50 seconds for enrollment and 22 seconds for Verification. High security due to palm vein encryption, effective against biometric spoofing, and the multimodal approach ensures reliability if one biometric fails. Potential for integration with smart cards or on-card systems, enhancing security in personal identification systems.