| dc.description.abstract | Since the biometric solution is able to avoid the security problems which the traditional method may be compromised, for example, the password may be compromised by brute force attack or phishing attack, many systems gradually use biometrics to replace traditional passwords to achieve authentication functions. However, other potential defects of biometric are inevitably caused, such as the immutability of physiological characteristics, and the lower recognition rate in unconditional environment.
As a part of biometric technology, iris recognition has the advantages of features persistency and high recognition rate. On the other hand, face recognition benefits from lower equipment costs and maintains a certain recognition rate. Therefore, both authentication systems are commonly used for authentication services in business or military access control. However, the accuracy of the biometric system mainly depends on the features of high-degree-of-freedom. The low-resolution image will cause the loss of feature details and greatly reduce the biometric recognition rate. Therefore, the traditional solution attempt to apply the high-cost sampling equipment to achieve higher resolution images.
With the technology of the Internet of Things (IoT) become mature, the number of the IoT device will be expected to grow to 50 billion around the world in 2030, and the various service is already prepared to deploy on it. According to the traditional solution of iris recognition, the deployment of expensive image acquisition sensors on mobile devices will inevitably result in high retail price. However, the low-cost equipments may not be able to meet various requirements based on image resolution, such as the additive photosensitive element to catch the depth of field, the additive storage to store the high-resolution image, and the high bandwidth to transmit the high-resolution image.
In order to deal with above mentioned issue, the super-resolution technology have been proposed to generate the super-resolution image of biometrics from the low-resolution input. Benefit from clear detailed information of output biometrics image, the overall recognition system will be greatly improved, and reduce the cost of building low-end equipment indirectly. | en_US |