Background: Extracting important descriptors and features from images of biological specimens is an ongoing challenge. Features are often defined using landmarks and semi-landmarks that are determined a priori based on criteria such as homology or some other measure of biological significance. An alternative, widely used strategy uses computational pattern recognition, in which features are acquired from the image de novo. Subsets of these features are then selected based on objective criteria. Computational pattern recognition has been extensively developed primarily for the classification of samples into groups, whereas landmark methods have been broadly applied to biological inference. Results: To compare these approaches and to provide a general community resource, we have constructed an image database of Drosophila melanogaster wings - individually identifiable and organized by sex, genotype and replicate imaging system - for the development and testing of measurement and classification tools for biological images. We have used this database to evaluate the relative performance of current classification strategies. Several supervised parametric and nonparametric machine learning algorithms were used on principal components extracted from geometric morphometric shape data (landmarks and semi-landmarks). For comparison, we also classified phenotypes based on de novo features extracted from wing images using several computer vision and pattern recognition methods as implemented in the Bioimage Classification and Annotation Tool (BioCAT). Conclusions: Because we were able to thoroughly evaluate these strategies using the publicly available Drosophila wing database, we believe that this resource will facilitate the development and testing of new tools for the measurement and classification of complex biological phenotypes.

/pdf/an-image-database-of-drosophila-melanogaster-wings-for-403aarz20h.pdf

An image database of Drosophila melanogaster wings for phenomic and biometric analysis

The biometric system is an automatic identification and authentication system that uses unique biological traits, such as fingerprint, face, iris, voice, retina, etc. of an individual. Of all these systems, fingerprint biometric system is the most widely used because of its low cost, high matching speed, and relatively high matching accuracy. Due to the high efficiency of fingerprint biometric system in verifying a legitimate user, numerous government and private organizations are using this system for security purpose. This paper provides an overview of the fingerprint biometric system and gives details about various current security aspects related to the system. The security concerns that we address include multiple attacks on the system, associated threat models, biometric cryptosystems, current issues, challenges, opportunities, and open problems that exist in present day fingerprint biometric systems

Security Vulnerabilities Against Fingerprint Biometric System.

In this paper, we present an iris-based access control protocol that is resistant to iris-based replay attacks. This new style of biometric-based access control protocol is similar to the so called, 'one time password' approach used by some conventional username/password access protocols. Our results show that not only is this new type of iris-based access protocol effective, it can also distinguish between when a user attempting to gain access has supplied a poor sample of their iris and when the access control system is experiencing an iris-based replay attack.

/pdf/mitigating-iris-based-replay-attacks-328dazkkz5.pdf

Mitigating Iris-Based Replay Attacks

In the current technology advancement, biometric template provides a dependable solution to the problem of user verification in an identity control system. The template is saved in the database during the enrollment and compared with query information in the verification stage. Serious security and privacy concerns can arise, if raw, unprotected data template is saved in the database. An attacker can hack the template information in the database to gain illicit access. A novel approach of encryption-decryption algorithm utilizing a design pattern of Model View Template (MVT) is developed to secure the biometric data template. The model manages information logically, the view shows the visualization of the data, and the template addresses the data migration into pattern object. The established algorithm is based on the cryptographic module of the Fernet key instance. The Fernet keys are combined to generate a multiFernet key to produce two encrypted files (byte and text file). These files are incorporated with Twilio message and securely preserved in the database. In the event where an attacker tries to access the biometric data template in the database, the system alerts the user and stops the attacker from unauthorized access, and cross-verify the impersonator based on the validation of the ownership. Thus, helps inform the users and the authority of, how secure the individual biometric data template is, and provided a high level of the security pertaining the individual data privacy.

/pdf/developing-an-algorithm-for-securing-the-biometric-data-2d6yo4i5oa.pdf

Developing an Algorithm for Securing the Biometric Data Template in the Database

A biometric replay attack occurs when a victim's biometric information (in the form of a template) is stolen by a hacker as it is transmitted over a network and used by the hacker to impersonate the victim to gain unauthorized access. Biometric replay attacks can be particularly devastating because the victims of these attacks cannot easily alter their biometrics or the templates associated with their biometrics [1]. In an effort to mitigate biometric replay attacks, the authors of [2] proposed two biometric authentication protocols. These protocols are based on two principles: (a) it is possible to evolve a set of distinct feature extractors (FEs) and (b) distinct FEs produce distinct templates. These principles gave rise to the concept of disposable FEs (and templates). In this paper, we introduce two additional protocols based on disposable FEs and templates. Our results show that these new protocols can be used more efficiently than the two proposed in [2].

Permutation-based biometric authentication protocols for mitigating replay attacks

Genetic & Evolutionary Biometric Security: Disposable Feature Extractors for Mitigating Biometric Replay Attacks

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Genetic & Evolutionary Biometric Security: Disposable Feature Extractors for Mitigating Biometric Replay Attacks