Iris feature extraction and recognition using Wavelet Packet Analysis

Hariprasath, S.; Venkatasubramanian, Suresh

doi:10.1109/icsip.2010.5697465

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…In this work different technique of fusion of the two types like Decision level fusion, score level fusions are compared. The algorithms used for extracting region of interest of Iris are less complex as against [4,7]. Features extracted from individual modalities gave better results as compared to [2,6].…”

Section: Discussionmentioning

confidence: 96%

“…This involved first employing Canny edge detection to generate an edge map. The gradients thus generated were biased in the vertical direction for the outer iris/sclera boundary, as concluded by Wildes et al [4]. Vertical and horizontal gradients were then weighted equally for the inner iris/pupil boundary.…”

Section: Pre-processing For Iris Imagesmentioning

confidence: 99%

“…Hariprasath and S. Venkatsubramaniam have proposed an iris recognition system based on wavelet packet analysis in their paper "Iris feature extraction and recognition using wavelet analysis" [4]. They have proposed a multi-resolution approach based on Wavelet Packet Transform (WPT) for iris texture analysis and recognition.…”

Section: Prior Workmentioning

confidence: 99%

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Multimodal Biometric Identification System: Fusion of Iris and Fingerprint

PPatil¹,

Raka²,

Sarode³

2014

IJCA

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This paper proposes a biometric person identification system based on fusion of Iris and Fingerprint images. This paper attempts to improve the performance of iris and fingerprintbased identification system by combining the individual features obtained by implementing Iris and Fingerprint identification system. There exist techniques to provide biometric identification systems based on one or other type of modalities. But usage of multiple modalities has resulted in higher accuracy and reliability as concluded by the experiments conducted.

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Section: Discussionmentioning

confidence: 96%

Section: Pre-processing For Iris Imagesmentioning

confidence: 99%

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Multimodal Biometric Identification System: Fusion of Iris and Fingerprint

PPatil¹,

Raka²,

Sarode³

2014

IJCA

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“…The approach proposed by Hariprasath and Venkatasubramanian [10] is based on 2D WPT. First, iris region is encoded into a sequence of 2D wavelet packet coefficients with a size of the feature vector of 1280 bits.…”

Section: Related Workmentioning

confidence: 99%

Multilinear principal component analysis for iris biometric system

Kamlaskar

Abhyankar

2021

IJEECS

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<p>Iris biometric modality possesses inherent characteristics which make the iris recognition system highly reliable and noninvasive. Nowadays, research in this area is challenging compact template size and fast verification algorithms. Special efforts have been employed to minimize the size of the extracted features without degrading the performance of the iris recognition system. In response, we propose an improved feature fusion approach based on multilinear subspace learning to analyze Iris recognition. This approach consists of four stages. In the first stage, the eye image is segmented to extract the iris region. In the second step, wavelet packet decomposition is conducted to extract features of the iris image, since good time and frequency resolutions can be provided simultaneously by the wavelet packet decomposition. In the next step, all decomposed nodes or packets are arranged as a 3<sup>rd</sup> order tensor rather than a long vector, in which feature fusion is directly implemented with multilinear principal component analysis (MPCA). This approach provides a more compact or useful low-dimensional representation directly from the original tensorial representation. Finally, a discriminative tensor feature selection mechanism and classification strategy are applied to iris recognition problem. The obtained results indicate the usefulness of MPCA to select discriminative features and fuse them effectively. The experimental results reveal that the proposed tensor-based MPCA approach achieved a competitive matching performance on the SDUMLA-HMT Iris database with an adequate acceptable rate.</p>

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