Iris Recognition based on Displacement Information using a Sparse Matching Technique Iris recognition is one of the most promising fields in biometrics. During the image acquisition, images are affected by surrounding parts of the eye such as eyelids or eyelashes. Besides, deformations of the iris image are produced due to preprocessing procedures. In this work, a new iris recognition method is presented based on the displacement information. This approach is based on the fact that deformations produced in the iris images can be used as discriminative information. Our proposal introduces the use of image matching based on optical flow to the iris recognition area. The proposed methodology uses the DAISY descriptor combined with the algorithm CPM, for feature extraction and matching processes, respectively. The experimental results on the CASIAIrisInterval dataset demonstrate the high accuracy of the proposed method. Moreover, comparison with other approaches based on seed points shows that it improves their results and it is competitive with other techniques.

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.