Performance evaluation of iris-based recognition system implementing PCA and ICA encoding techniques

Dorairaj, Vivekanand; Schmid, Natalia A.; Fahmy, Gamal

doi:10.1117/12.604201

Cited by 64 publications

(31 citation statements)

References 12 publications

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“…Comparison of FAR and FRR using JPEG 2000 compression was done on 2 datasets for each of the normalization performed through classic Daughman's rubber sheet model and non linear biomechanical model [14]. The analysis was done as per Table 1 below …”

Section: Resultsmentioning

confidence: 99%

Comparison of Detection Accuracy and Effect of JPEG2000 Compression on Iris Recognition

Kapoor¹,

Rawat²

2017

IJCA

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In today's digital world, identification based on biometrics has received much attention from research community as well as from industries for security applications. Iris recognition is evolving as one of the most active techniques in biometrics technology accounting to its high reliability for identification and is proved to be most error free means to identify persons. Iris is considered as the reliable biometric feature based on its uniqueness and robustness. To perform iris recognition iris/eye image is captured from numerous person's and these images should be stored in the data base & retrieved whenever required. Hence there is need of huge databases of iris images. Compression is a unique option available if available storage space is not sufficient for the images. Compression empowers a reduction in the space needed to store these iris images. The aim of this paper is to present the effects of iris image compression on the recognition performance. Usually iris images are 600 times bigger than the Iris Code templates which requires enormous space for storage. It is expected that iris data should be secured, transmitted and embedded in media in the form of images instead of templates. To obtain this objective considering its implications for bandwidth and storage, this paper presents the scheme that combine ROI(region-of-interest) isolation with JPEG 2000 compression at different levels using publicly available database of iris images each in case of two cases of Normalized iris images on with classic Daughman's rubber sheet model and the second one through non-linear Biomechanical model. It is concluded that JPEG 2000 compression gives the better results with iris images normalized with Biomechanical model with minimum impact on recognition performance.

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Section: Resultsmentioning

confidence: 99%

Comparison of Detection Accuracy and Effect of JPEG2000 Compression on Iris Recognition

Kapoor¹,

Rawat²

2017

IJCA

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“…There are researches used Hamming distances for matching [5][6][7][8]. The other research used Normalized correlation for matching [9].In [10][11] used Euclidean distance for matching. In [12][13][14] used Band-limited phase only correlation for matching.…”

Section: Matching With Correlationmentioning

confidence: 99%

Iris Matching Using SURF Algorithm

Mohammed¹,

Habeeb²,

Abood³

2016

IJSIP

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Iris recognition is one of biometric techniques that provide fast and accurate for human identification. This paper studies four main types of iris (Jewel, flower, stream, and shaker). And also it studies a fast method using Speed Up Robust Features algorithm (SURF) for finding a match between original eye image and input eye image to the system with taking into account the speed of algorithm implementation and its compatibility with an iris scanner.

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“…Evaluating the performance of iris recognition algorithms is a difficult issue [26], [33]. For the purpose of evaluation; we implement iris recognition method according to the published papers [13], [24].…”

Section: Figure 9 Precision Recall Vs Query Imagesmentioning

confidence: 99%

Performance Evaluation in Iris Recognition and CBIR System based on Phase Congruency

Patil¹,

Kolhe²,

Patil³

et al. 2012

IJCA

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Content based image retrieval is an important research area in image processing, with a vast domain of applications like recognition systems i.e. face, finger, iris biometric etc. It retrieves the similar type of images from repository of images based on users query. To retrieve similar images, color, and texture or shape features need to be extracted from the images and stored in the feature database. The color, texture or shape features of query image are compared with the features of images in the database. This comparison is performed using color, texture or shape distance metrics. Phase Congruency is applied to the input image for feature extraction. Advantage of the Phase Congruency method is its insensitivity to variations in image illumination and contrast. Like CBIR, the methods for iris recognition mainly focus on feature representation and matching. We use phase congruency to generate iris feature vector. Phase Congruency can be calculated using Log Gabor wavelets. The goal of this paper is to evaluate the performance of CBIR system and iris recognition that use Phase Congruency. To evaluate performance in CBIR System, precision and recall measures are used. In order to evaluate iris recognition system, recognition rate measure is used and compared with existing methods. Results of performance evaluation are discussed in paper.

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Performance evaluation of iris-based recognition system implementing PCA and ICA encoding techniques

Cited by 64 publications

References 12 publications

Comparison of Detection Accuracy and Effect of JPEG2000 Compression on Iris Recognition

Comparison of Detection Accuracy and Effect of JPEG2000 Compression on Iris Recognition

Iris Matching Using SURF Algorithm

Performance Evaluation in Iris Recognition and CBIR System based on Phase Congruency

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