UFace: Your universal password no one can see

With the advantage of not having to memorize long passwords, facial authentication has become a topic of interest among researchers. However, since many users store images containing their face on social networking sites, a new challenge emerges in preventing attackers from impersonating these users by using these online photos. Another problem with most current facial authentication protocols is that they require an unencrypted image of each registered user\u27s face to compare against. Moreover, they might require the user\u27s device to execute computationally expensive multiparty protocols which presents a problem for mobile devices with limited processing power. Finally, these authentication protocols will not be able to be implemented in real systems because they take too long to execute. In this paper, we present a novel privacy preserving facial authentication system, called UFace. Not only does UFace limit the amount of computation for a user\u27s mobile device, but it also prevents unencrypted images from leaving a user\u27 possession while finishing the authentication protocol within seconds. Web services can now outsource their authentication protocol to UFace so that each web service only needs to handle its own functionality. UFace guarantees that it can correctly authenticate each user with 90% accuracy, prevent attacks from using online photos and that all data used in the authentication protocol is done on encrypted randomized data. In other words, only the user can see the facial image and feature vector used for authentication; all other parties execute the protocol using seemingly random information. UFace was implemented through two facets: a mobile client application to obtain and encrypt the feature vector of each user\u27s facial image, and a server protocol to securely authenticate a feature vector using secure multiparty computations. The experimental results demonstrate that UFace can be used as a third party authentication tool for any number of web services --Abstract, page iii

Multimodal decision-level fusion for person authentication

In this paper, the use of clustering algorithms for decision-level data fusion is proposed. Person authentication results coming from several modalities (e.g., still image, speech), are combined by using fuzzy k-means (FKM), fuzzy vector quantization (FVQ) algorithms, and median radial basis function (MRBF) network. The quality measure of the modalities data is used for fuzzification. Two modifications of the FKM and FVQ algorithms, based on a novel fuzzy vector distance definition, are proposed to handle the fuzzy data and utilize the quality measure. Simulations show that fuzzy clustering algorithms have better performance compared to the classical clustering algorithms and other known fusion algorithms. MRBF has better performance especially when two modalities are combined. Moreover, the use of the quality via the proposed modified algorithms increases the performance of the fusion system

An Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image

Biometrics based personal identification is regarded as an effective method for automatically recognizing, with a high confidence a person’s identity. A multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically better recognition performance compare to system based on a single biometric modality. This paper proposes an authentication method for a multimodal biometric system identification using two traits i.e. face and palmprint. The proposed system is designed for application where the training data contains a face and palmprint. Integrating the palmprint and face features increases robustness of the person authentication. The final decision is made by fusion at matching score level architecture in which features vectors are created independently for query measures and are then compared to the enrolment template, which are stored during database preparation. Multimodal biometric system is developed through fusion of face and palmprint recognition