A Hybrid Classification Approach for Iris Recognition System for Security of Industrial Applications

The biometric authentication system is demanded to identify a particular person from the set of persons. Even though many biometric authentication methods are available such as fingerprint, palm, face, and iris, the iris-based recognition system is effective due to its simplified process. This article proposes an iris recognition system using a hybrid classification approach for security applications. The proposed method includes three modules: preprocessing, augmentation, and classifier. The preprocessing module converts the color iris images into grey scale images and also resizes the image into 256 × 256. The preprocessed iris images are now data augmented to construct the larger dataset. The data augmented images are classified into either genuine or imposter images using a hybrid classification approach. The hybrid classification approach functions in two modes as training and testing. In this article, the Convolutional Neural Networks (CNN) is integrated with the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier to enhance the recognition rate of the iris recognition system. The performance analysis of the proposed approach is shown in terms of sensitivity, accuracy, recognition rate, specificity, false-positive rate, and false-negative rate. The experimental results of the proposed iris recognition system stated in this article significantly outweigh other design methods

Thoracic manifestations of paradoxical immune reconstitution inflammatory syndrome during or after antituberculous therapy in HIV-negative patients

Immune reconstitution inflammatory syndrome (IRIS) is a consequence of exaggerated and dysregulated host’s inflammatory response to invading microorganism, leading to uncontrolled inflammatory reactions. IRIS associated with tuberculosis (TB) is well recognized among human immunodeficiency virus (HIV)-infected patients receiving highly active antiretroviral therapy, but it is less common among HIV-negative patients. IRIS can manifest as a paradoxical worsening or recurring of preexisting tuberculous lesions or development of new lesions despite successful antituberculous treatment. Hence, the condition might be misdiagnosed as superimposed infections, treatment failure, or relapse of TB. This pictorial essay reviewed diagnostic criteria and various thoracic manifestations of the paradoxical form of TB-associated IRIS (TB-IRIS) that might aid in early recognition of this clinical entity among HIV-negative patients. The treatment and outcomes of TB-IRIS were also discussed

Eye contrast polarity is critical for face recognition by infants

Just as faces share the same basic arrangement of features, with two eyes above a nose above a mouth, human eyes all share the same basic contrast polarity relations, with a sclera lighter than an iris and a pupil, and this is unique among primates. The current study examined whether this bright-dark relationship of sclera to iris plays a critical role in face recognition from early in development. Specifically, we tested face discrimination in 7- and 8-month-old infants while independently manipulating the contrast polarity of the eye region and of the rest of the face. This gave four face contrast polarity conditions: fully positive condition, fully negative condition, positive face with negated eyes ( negative eyes ) condition, and negated face with positive eyes ( positive eyes ) condition. In a familiarization and novelty preference procedure, we found that 7- and 8-month-olds could discriminate between faces only when the contrast polarity of the eyes was preserved (positive) and that this did not depend on the contrast polarity of the rest of the face. This demonstrates the critical role of eye contrast polarity for face recognition in 7- and 8-month-olds and is consistent with previous findings for adults

SHE based Non Interactive Privacy Preserving Biometric Authentication Protocols

Being unique and immutable for each person, biometric signals are widely used in access control systems. While biometric recognition appeases concerns about password's theft or loss, at the same time it raises concerns about individual privacy. Central servers store several enrolled biometrics, hence security against theft must be provided during biometric transmission and against those who have access to the database. If a server's database is compromised, other systems using the same biometric templates could also be compromised as well. One solution is to encrypt the stored templates. Nonetheless, when using traditional cryptosystem, data must be decrypted before executing the protocol, leaving the database vulnerable. To overcame this problem and protect both the server and the client, biometrics should be processed while encrypted. This is possible by using secure two-party computation protocols, mainly based on Garbled Circuits (GC) and additive Homomorphic Encryption (HE). Both GC and HE based solutions are efficient yet interactive, meaning that the client takes part in the computation. Instead in this paper we propose a non-interactive protocol for privacy preserving biometric authentication based on a Somewhat Homomorphic Encryption (SHE) scheme, modified to handle integer values, and also suggest a blinding method to protect the system from spoofing attacks. Although our solution is not as efficient as the ones based on GC or HE, the protocol needs no interaction, moving the computation entirely on the server side and leaving only inputs encryption and outputs decryption to the client