I-Corps: Many-Core Computing for Biometrics Liveness Detection

生物识别（例如指纹识别）在安全访问所需的身份识别和验证方面比传统方法（例如密码或安全卡）具有许多优势。实现生物识别的算法可能是计算密集型的，但是生物识别系统的用户需要快速、方便和准确的识别和认证过程。在这个项目中，团队？的具体重点是创建一个指纹活性算法（检测是否呈现指纹是活皮肤或欺骗），以提高指纹检测的准确性和安全性。通过在云上计算该算法，该团队将能够提高执行速度和/或扫描的准确性，并使整个认证系统更适用于不同的环境。在该提案中，该团队建议开展一个项目，涉及将多核平台上的生物识别应用程序的并行化从研究转移到工业。众核计算的好处和用途已经得到了很好的研究，先进的生物识别算法也是如此。拟议的工作将把与众核计算和并行化相关的研究应用于生物识别领域，以证明可行产品的性能改进。具体来说，该团队将专注于指纹活性检测算法，该算法可与识别或验证结合使用。这项工作将有助于提高这些系统提供的安全性，并将证明众核和云计算中的既定方法和概念可以应用于这一领域。

Biometric recognition (for example, fingerprint recognition) offers many advantages over traditional methods (such as passwords or security cards) for identification and verification required for secure access. The algorithms enabling biometric recognition can be computationally intensive, but users of biometrics systems require identification and authentication processes to be rapid, convenient, and accurate. In this proposal, the team?s specific focus is on creating a fingerprint liveness algorithm (which detects if a fingerprint presented is live skin or a spoof) to improve accuracy and security of fingerprint detection. By enabling this algorithm to be calculated on the cloud, the team will be able to improve speed of execution and/or accuracy of scans and make the overall authentication system more feasible for use in different contexts.In this proposal, the team proposes to undertake a project involving the transfer of parallelization of biometric applications on many-core platforms from research to industry. The benefits and uses of many-core computing are well studied already, as are advanced biometrics algorithms. The work being proposed will apply research related to many-core computing and parallelization to the field of biometrics in order to demonstrate performance improvements for a viable product. Specifically, the team will focus on a fingerprint liveness detection algorithm that can be used in conjunction with identification or verification. The work will help improve the security offered by such systems, and will demonstrate that established methods and concepts in many-core and cloud computing can be applied to this field.