Ageing Adaptation for Multimodal Biometrics using Adaptive Feature Set Update Algorithm

Multimodal Biometrics is an emerging domain in biometric technology where more than one biometric trait is combined to improve the performance. The biometric system take Face, Fingerprint, Voice, Handwritten Signatures, Retina, Iris, Gait, Palm print, Ear & Hand geometry as common features. Human is identified by correct matching of these features. However, features like face, voice, and signature have low permanence and they change with time. Ageing of human, as well as other psychological & environmental conditions cause gradual change in these features. While enrolling feature set we don´t consider this factor. Here we propose a new concept that can be used in designing future multimodal biometrics systems which can adapt to the change in the biometrics features like face, voice, signature, and gait over the time or any other factor without compromising the security. Regression based technique can be used to detect change. This algorithm requires use of at least one biometric feature which has very low variance or high degree of permanence, like Fingerprint, Iris, Retina etc. This algorithm can address the problem of false rejection caused by sustained change in biometric features due to Ageing or any other factor without the need of re-enrollment of feature set.