Moments are widely-used feature extractors due to their superior discriminatory power and geometrical invariance. Unfortunately, moments suffer heavy computational load and result long time spending. In viewing of the problem, we proposed a new technique in using moments-apply moments on wavelet subband. In this study, pseudo Zernike moments are selected as feature extractors due to its enhanced feature representation capability. Implementation of moments on wavelet subband affords advantages of performing local-to-global analysis and decomposing image into lower resolution. Experimental results show that this hybrid achieves computational time reduction of 36.23% with enhanced authentication performance.
Abstract:A novel authentication approach which based on the integration of tokenised pseudo-random numbers and the user's iris biometrics, to generate a user-specific binary bitstring via an iterated inner-product mechanism is proposed. The proposed method, S-Iris encoding, protects against iris fabrication as it can only contribute to the authentication process when both the genuine biometrics template and pseudo-random number are presented. It also enables straightforward revocation via token replacement. Furthermore, S-Iris Encoding reduces length of iris feature to around 5% of its original size yet able to attain an approximately 0% of Equal Error Rate (EER). This approach also shows the effectiveness and accuracy in term of verification rate if compared to the traditional biometrics system or the straightforward use of two authentication schemes in parallel.
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