Evaluation of LBPbased descriptors for Iris Recognition based on Learning Vector Quantization Classifier under a Multicore Platform In recent years, iris recognition techniques have focused the attention on the biometric area. Particularly, the performance of an iris recognition system depends on the representation of the texture of the images. In this sense, Local Binary Patterns (LBP) have emerged as one of the most prominent and widely studied local texture descriptors. This paper aims to evaluate the use of different LBPbased descriptors for iris recognition by using a Learning Vector Quantization Classifier (LVQ). Our study shows the first analysis of the performance of LVQ classifier on different LBPbased descriptors for iris recognition. Besides, the proposed method evaluates the impact of a parallel implementation of the system under a multicore platform. The evaluation carried out on the CASIAIrisInterval database, shows that LVQ classifier is an effective alternative for iris recognition using LBPbased descriptors. The proposed method achieved a recognition rate of 98.36% and obtaining a speedup of 24.81x.

Iris Feature Extraction and Matching Method for Mobile Biometric Applications Biometric methods are increasingly penetrating the field of mobile applications, confronting researchers with a huge number of problems that have not been considered before. Many different interaction scenarios in conjunction with the mobile device performance limitations challenge the capabilities of onboard biometrics. Saturated with complex textural features the iris image is used as a source for the extraction of unique features of the individual that are used for recognition. The mentioned features inherent to the interaction with the mobile device affect not only the source image quality but natural deformations of the iris leading to high intraclass variations and hence reducing the recognition performance. A novel method for iris feature extraction and matching is represented in this work. It is based on a lightweight CNN model combining the advantages of a classic approach and advanced deep learning techniques. The model utilizes shallow and deep feature representations in combination with characteristics describing the environment that helps to reduce intraclass variations and as a consequence the recognition errors. It showed high efficiency on the mobile and a few more datasets outperforming stateoftheart methods by far.