Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud

Biometric recognition, or simply biometrics, is the use of biological attributes such as face, fingerprints or iris in order to recognize an individual in an automated manner. A key application of biometrics is authentication; i.e., using said biological attributes to provide access by verifying the claimed identity of an individual. This paper presents a framework for Biometrics-as-a-Service (BaaS) that performs biometric matching operations in the cloud, while relying on simple and ubiquitous consumer devices such as smartphones. Further, the framework promotes innovation by providing interfaces for a plurality of software developers to upload their matching algorithms to the cloud. When a biometric authentication request is submitted, the system uses a criteria to automatically select an appropriate matching algorithm. Every time a particular algorithm is selected, the corresponding developer is rendered a micropayment. This creates an innovative and competitive ecosystem that benefits both software developers and the consumers. As a case study, we have implemented the following: (a) an ocular recognition system using a mobile web interface providing user access to a biometric authentication service, and (b) a Linux-based virtual machine environment used by software developers for algorithm development and submission

Multibiometric Secure System Based on Deep Learning

In this paper, we propose a secure multibiometric system that uses deep neural networks and error-correction coding. We present a feature-level fusion framework to generate a secure multibiometric template from each user's multiple biometrics. Two fusion architectures, fully connected architecture and bilinear architecture, are implemented to develop a robust multibiometric shared representation. The shared representation is used to generate a cancelable biometric template that involves the selection of a different set of reliable and discriminative features for each user. This cancelable template is a binary vector and is passed through an appropriate error-correcting decoder to find a closest codeword and this codeword is hashed to generate the final secure template. The efficacy of the proposed approach is shown using a multimodal database where we achieve state-of-the-art matching performance, along with cancelability and security.Comment: To be published in Proc. IEEE Global SIP 201

Integration of Deep Hashing and Channel Coding for Biometric Security and Biometric Retrieval

In the last few years, the research growth in many research and commercial fields are due to the adoption of state of the art deep learning techniques. The same applies to even biometrics and biometric security. Additionally, there has been a rise in the development of deep learning techniques used for approximate nearest neighbor (ANN) search for retrieval on multi-modal datasets. These deep learning techniques knows as deep hashing (DH) integrate feature learning and hash coding into an end-to-end trainable framework. Motivated by these factors, this dissertation considers the integration of deep hashing and channel coding for biometric security and different biometric retrieval applications. The major focus of this dissertation is biometric security, wherein deep hashing is integrated with channel coding to develop a secure biometric authentication system. In this system, multiple biometric modalities of a single user are combined at the feature level using deep hashing (binarization). A hybrid secure architecture that combines cancelable biometrics with secure sketch techniques is integrated with the deep hashing framework, which makes it computationally prohibitive to forge a combination of multiple biometrics that passes the authentication. The integration of deep hashing and channel coding not only finds application in biometric security but it can also be extended to different biometric applications. To this end, the integration of deep cross-modal hashing and error correcting codes has been extended to improve the efficiency of attribute-guided face image retrieval. Additionally, the dissertation also presents a framework for cross-resolution (low-resolution to high-resolution) face recognition, and profile-to-frontal face recognition. A novel attribute- guided cross-resolution (low-resolution to high-resolution) face recognition system that lever- ages a coupled generative adversarial network (cpGAN) structure with adversarial training to find the hidden relationship between low-resolution and high-resolution images in a latent common embedding subspace is developed and presented. A similar framework that leverages cpGAN structure has been developed for a profile-to-frontal face recognition system. Finally, the performance of this cpGAN architecture for profile-to-frontal face recognition system has been evaluated and compared with a coupled convolutional neural network (cpCNN) and an adversarial discriminative domain adaptation (ADDA) network