Periocular recognition in cross-spectral scenario

Behera, Sushree S.; Gour, Mahesh; Kanhangad, Vivek; Puhan, Niladri B.

doi:10.1109/btas.2017.8272757

Cited by 20 publications

(13 citation statements)

References 20 publications

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“…Table IV points out that the approach which Behera et al [8] followed produced better results than the approach followed by Ramaiah et al [9]. The results of the current study prove to be very good relative to the results of these two studies (Table IV), with noticeable differences.…”

Section: Discussion With Comparison With Previous Approachessupporting

confidence: 58%

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Local Binary Pattern Method (LBP) and Principal Component Analysis (PCA) for Periocular Recognition

Alkhazali¹,

El-Bashir²

2020

IJACSA

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Identification of identity through eye is gaining more and more importance. Commonly, the researchers approach the eye from any of three parts, the iris, the circumference around the eye, and the iris and its circumference. This study follows a holistic approach to identity identification by using the iris and whole periocular area and proposes a periocular recognition system (PRS) that has been developed using the Local Binary Pattern (LBP) technique combined with Principal Component Analysis (PCA) at the feature extraction stage and the k-nearest neighbors (k-NN) algorithm as a classifier at the classification stage. This system achieves identity recognition through three steps: pre-processing, feature extraction, and classification. Pre-processing is applied to the images so as to convert them to grayscale. In the feature extraction step, the LBP method is applied to extract the texture feature from the images and use it in PCA to reduce data dimensionality and obtain the relevant data so that only the important features are extracted. These two steps are applied both in the training phase and the testing phase of image processing. On the other hand, the testing data sets are processed using the k-NN classifier. The proposed PRS was tested on data drawn from the PolyU database using more than one basis of system experience. Specifically, the system performance was tested once on all 209 subjects present in the database and once on 140 subjects. This database also contains images taken in the visible (VIS) and near-infrared (NIR) regions of the electromagnetic radiation (EMR) spectrum. So, the system was tested on images taken in both regions separately for matching. As well, the proposed PRS benefited from the availability of images for the right and left perioculars. Performance was, therefore, tested on images of each side of the periocular area (the left and right sides) separately, as well as for the combination of the two sides. The identity recognition rates characteristic of the proposed PRS were most often higher than the recognition rates produced by systems reported in the literature. The highest recognition accuracy obtained from the proposed system, which is 98.21%, was associated with the 140subject data subset .

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Section: Discussion With Comparison With Previous Approachessupporting

confidence: 58%

“…Behera et al [8] underscored that periocular recognition had been active domain of research in the last few years. They suggested an identity recognition method that is based on illumination normalization of NIR and VIS periocular images.…”

Section: Literature Reviewmentioning

confidence: 99%

Local Binary Pattern Method (LBP) and Principal Component Analysis (PCA) for Periocular Recognition

Alkhazali¹,

El-Bashir²

2020

IJACSA

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“…Apart from the accuracies observed, we have made following observations: Cross-Database Performance: In order to compare the performance of the proposed approach with state-of-the-art algorithms [4,25] for the IMP dataset, we performed testing on this dataset without training on any image of this dataset. The deep CNN model was trained on the CASIA NIR-VIS 2.0 dataset [16].…”

Section: Resultsmentioning

confidence: 99%

“…The proposed algorithm is evaluated by using the model trained on cropped images of the CASIA NIR-VIS 2.0 face database [16]. This is done in order to keep the protocols consistent (to perform comparison) with other cross-spectral periocular recognition methods namely Behera et al [4] and Ramaiah et al [25].…”

Section: Iiitd Multi-spectral Periocular Databasementioning

confidence: 99%

Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition

Rishabh

Baweja

Ghosh

et al. 2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology. However, this brings an additional challenge of recognizing biometric patterns in an unconstrained environment including variations in mobile camera sensors, illumination conditions, and capture distance. To address the heterogeneous challenge, this research presents a novel heterogeneity aware loss function within a deep learning framework. The effectiveness of the proposed loss function is evaluated for periocular biometrics using the CSIP, IMP and VISOB mobile periocular databases. The results show that the proposed algorithm yields state-of-the-art results in a heterogeneous environment and improves generalizability for crossdatabase experiments.

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“…Previous works on periocular recognition applied traditional features used in biometric recognition, especially, in face recognition. Examples of works employ Histograms of Oriented Gradients (HOGs) [17]- [19], Local Binary Patterns (LBPs) [8], [17], [19]- [22], Principal Component Analysis (PCA) [22] and Scale-Invariant Feature Transform (SIFT) [8], [17], [18], etc. The recognition methods using these features are relatively robust against imperfect alignment and changes in facial expression.…”

Section: Related Workmentioning

confidence: 99%

Phase-Based Periocular Recognition with Texture Enhancement

Marval-Perez

Ito

Aoki

2019

IEICE Trans. Fundamentals

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Access control and surveillance applications like walkingthrough security gates and immigration control points have a great demand for convenient and accurate biometric recognition in unconstrained scenarios with low user cooperation. The periocular region, which is a relatively new biometric trait, has been attracting much attention for recognition of an individual in such scenarios. This paper proposes a periocular recognition method that combines Phase-Based Correspondence Matching (PB-CM) with a texture enhancement technique. PB-CM has demonstrated high recognition performance in other biometric traits, e.g., face, palmprint and finger-knuckle-print. However, a major limitation for periocular region is that the performance of PB-CM degrades when the periocular skin has poor texture. We address this problem by applying texture enhancement and found out that variance normalization of texture significantly improves the performance of periocular recognition using PB-CM. Experimental evaluation using three public databases demonstrates the advantage of the proposed method compared with conventional methods.

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Periocular recognition in cross-spectral scenario

Cited by 20 publications

References 20 publications

Local Binary Pattern Method (LBP) and Principal Component Analysis (PCA) for Periocular Recognition

Local Binary Pattern Method (LBP) and Principal Component Analysis (PCA) for Periocular Recognition

Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition

Phase-Based Periocular Recognition with Texture Enhancement

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