Tata Consultancy Services Limited

Vasudha Kumari

Sachin Premsukh Lodha

Rajan Mindigal Alasingara Bhattachar

Srinivasa Rao Chalamala

Arun Kumar Jindal

Imtiyazuddin Shaik

Harika Narumanchi

Imtiyazuddir Shaik

Shaik Imtiyazuddin

Conventionally, biometric template protection has been achieved to improve matching performance with high levels of security by use of deep convolution neural network models. However, such attempts have prominent security limitations mapping information of images to binary codes is stored in an unprotected form. Given this model and access to the stolen protected templates, the adversary can exploit the False Accept Rate (FAR) of the system. Secondly, once the server system is compromised all the users need to be re-enrolled again. Unlike conventional systems and approaches, present disclosure provides systems and methods that implement encrypted deep neural network(s) for biometric template protection for enrollment and verification wherein the encrypted deep neural network(s) is utilized for mapping feature vectors to a randomly generated binary code and a deep neural network model learnt is encrypted thus achieving security and privacy for data protection.

Registration and verification of biometric modalities using encryption techniques in a deep neural network

This disclosure relates generally to a method and system for biometric verification. Conventional biometric verification method and system performs one or more computations in non-encrypted domain, thereby leading to security threats. The disclosed method includes performing computations such as enrollment and verification feature vector computation, dimensionality reduction of said feature vectors, and comparison of dimensionally reduced encrypted feature vectors to obtain matching scores indicating the extent of match therebetween between in encrypted domain using fully homomorphic encryption, thereby leading to secure biometric verification.

Method and system for biometric verification

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Pune, India

Vasudha Kumari