Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a dynamic biometric discretization scheme based on Linnartz and Tuyls's quantization index modulation scheme (LT-QIM) [Linnartz and Tuyls, 2003] is proposed. LT-QIM extracts one bit per feature element and takes care of the intra-class variation of the biometric features. Nevertheless, LT-QIM does not consider statistical distinctiveness beten users, and thus lacks the capability of preserving the discriminative por of the original biometric features.  put forward a generalized LT-QIM scheme in such a way that it allocates multiple bits to each feature element according to a statistical distinctiveness measure of the feature. Hence, more bits are assigned to high distinctive features and fer bits to low distinctive features. With provision for intra-class variation compensation and dynamic bit allocation by means of the statistical distinctiveness measure, the generalized scheme enhances the verification performance compared to the original scheme. Several comparative studies are conducted on two popular face data sets to justify the efficiency and feasibility of our proposed scheme. The security aspect is also considered by including a stolen-token scenario.
['Hyung Gu Lee', 'Andrew Beng Jin Teoh', 'Ho Gi Jung', 'Jaihie Kim']
I am writing a research paper on a idea A secure biometric discretization scheme for face template protection
[{"from": "human", "value": "I am writing a research paper on a idea A secure biometric discretization scheme for face template protection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a dynamic biometric discretization scheme based on Linnartz and Tuyls's quantization index modulation scheme (LT-QIM) [Linnartz and Tuyls, 2003] is proposed. LT-QIM extracts one bit per feature element and takes care of the intra-class variation of the biometric features. Nevertheless, LT-QIM does not consider statistical distinctiveness beten users, and thus lacks the capability of preserving the discriminative por of the original biometric features.  put forward a generalized LT-QIM scheme in such a way that it allocates multiple bits to each feature element according to a statistical distinctiveness measure of the feature. Hence, more bits are assigned to high distinctive features and fer bits to low distinctive features. With provision for intra-class variation compensation and dynamic bit allocation by means of the statistical distinctiveness measure, the generalized scheme enhances the verification performance compared to the original scheme. Several comparative studies are conducted on two popular face data sets to justify the efficiency and feasibility of our proposed scheme. The security aspect is also considered by including a stolen-token scenario."}]