This paper presents a shift, scale, and rotation-invariant technique for iris feature-representation and fused postclassification at the decision-level to improve the accuracy and speed of the iris-recognition system. Most of the iris-recognition systems are still incapable for providing low false rejections due to a wide variety of artifacts and are computationally inefficient. In order to address these problems, effective and computationally efficient iris features are extracted based on a new class of triplet half-band filter bank (THFB). First, a new class of THFB is designed by using generalized half-band polynomial suitable for iris feature extraction. This THFB satisfies perfect reconstruction (PR) and provides linear phase, regularity, better frequency-selectivity, near-orthogonality, and good time-frequency localization.
The uses of these properties are investigated to approximate iris features significantly. Second, a novel flexible k-out-of-n:A (Accept) postclassifier (any k-out-of-n-regions-Accept) is explored to achieve the robustness against possible intraclass iris variations. The proposed approach (THFB+ k-out-of-n:A)is capable of handling various artifacts, particularly segmentation error, eyelid/eyelashes occlusion, shadow of eyelids, head-tilt, and specular reflections during iris verification. Experimental results using UBIRIS, MMU1, CASIA-IrisV3, and IITD databases show the superiority of the proposed approach with some of the existing popular iris-recognition algorithms.Index Terms-Feature extraction, filter bank, half-band filters, iris recognition, k-out-of-n:A classifier, regularity, triplet half-band filter bank (THFB), wavelet transform.
In this article, new feature extraction methods, which utilize wavelet decomposition and reduced order linear predictive coding (LPC) coefficients, have been proposed for speech recognition. The coefficients have been derived from the speech frames decomposed using discrete wavelet transform. LPC coefficients derived from subband decomposition (abbreviated as WLPC) of speech frame provide better representation than modeling the frame directly. The WLPC coefficients have been further normalized in cepstrum domain to get new set of features denoted as wavelet subband cepstral mean normalized features. The proposed approaches provide effective (better recognition rate), efficient (reduced feature vector dimension), and noise robust features. The performance of these techniques have been evaluated on the TI-46 isolated word database and own created Marathi digits database in a white noise environment using the continuous density hidden Markov model. The experimental results also show the superiority of the proposed techniques over the conventional methods like linear predictive cepstral coefficients, Mel-frequency cepstral coefficients, spectral subtraction, and cepstral mean normalization in presence of additive white Gaussian noise.
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