Periocular biometrics: A survey

Kumari, Punam; Seeja, K. R.

doi:10.1016/j.jksuci.2019.06.003

Cited by 32 publications

(29 citation statements)

References 34 publications

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“…Recently, Kumari et al [21] noted that researchers are moving toward a deep-learning-based approach to increase recognition performance by lowering dependence on these feature extraction methods. As neural networks can learn useful expressions from the data provided and achieve significant improvements over previous methods, convolutional neural networks (CNN) have also been used for biometric techniques, such as face recognition [22] [23].…”

Section: Related Workmentioning

confidence: 99%

“…This enabled the achievement of state-of-the-art accuracy for various published datasets. However, as mentioned in [21], periocular authentication can still be improved through the development of feature extraction and learning methods. Therefore, we propose a CNN-based periocular authentication method, including a new feature extraction method, and we compared it with AttNet, a stateof-the-art technology, and hand-crafted feature-based methods (LBP, SIFT) [25].…”

Section: Related Workmentioning

confidence: 99%

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Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network

Hwang

Lee

2020

IEEE Access

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The biometric technique of iris recognition is considerably limited by the cost of optical devices and user inconvenience. Periocular-based methods are an alternative means of biometric authentication because they do not require expensive equipment. Moreover, the resulting data are suitable for biometrics because they include features such as eyelashes, eyebrows, and eyelids. However, conventional periocular-based biometric authentication methods use limited sets of features that are dependent on the selected feature extraction method, resulting in relatively poor performance. Therefore, we propose a deep-learning-based method that actively utilizes the various features contained in periocular images. The method maintains the mid-level features of the convolutional layers and selectively utilizes features useful for classification. We compared the proposed method with previous methods using public and self-collected databases. The experimental results show that the equal error rate is less than 1%, which is superior to the previous methods. In addition, we propose a new method to analyze whether mid-stage features have been utilized. As a result, it was confirmed that this approach, which utilizes the mid-level features, can effectively improve the feature extraction performance of the network. INDEX TERMS Convolutional neural networks, deep learning, mid-level features, periocular biometrics, periocular recognition.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network

Hwang

Lee

2020

IEEE Access

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“…In biometrics, a matching algorithm consists of two sequential steps: (1) feature template extraction and storing and (2) new biometric feature template matching with the previously stored biometrics data [8]. The feature template extraction and storing phase of a biometrics matching algorithm creates biometric data representation vector x in enrolment from a user U .…”

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Fully Homomorphic Encryption and Parallel Computation Based Biometric Data Matching

Çatak¹,

Yayilgan²,

Abomhara³

2020

Preprint

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One of the most reliable methods of authentication used today is biometric matching. This authentication process, which is done by using biometrics information such as fingerprint, iris, face, etc. is used in many application areas. Authentication at border gates is one of these areas. However, some restrictions have been introduced to storing and using such data, especially with the General Data Protection Regulation (GDPR). The main goal of this work is to find the practical implementation of fully homomorphic encryption-based biometric matching in border controls. In this paper, we propose a biometric authentication system based on hash expansion and fully homomorphic encryption features, considering these restrictions. One of the most significant drawbacks of the homomorphic encryption method is the long execution time. We solved this problem by executing the matching algorithm in parallel manner. The proposed scheme is implemented as proof-of-concept in the SMILE, and its advantages in privacy preservation has been demonstrated.

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“…Periocular authentication is an automated method of biometric identification that applies mathematical pattern recognition techniques on the video images of one or both of the eyes of a person, whose complex patterns are unique, stable, and can be seen from some distance. According to Kumari and Seeja [2], authentication based on the periocular region builds on features taken from both the face and iris. In this context, the periocular biometric expresses the facial region right close to the eye [2], and the periocular region, which is the area around the eye, encircles the eyebrows, eyelashes, eyelids, and the adjacent skin area [3].…”

Section: Introductionmentioning

confidence: 99%

“…According to Kumari and Seeja [2], authentication based on the periocular region builds on features taken from both the face and iris. In this context, the periocular biometric expresses the facial region right close to the eye [2], and the periocular region, which is the area around the eye, encircles the eyebrows, eyelashes, eyelids, and the adjacent skin area [3]. But nlike acquisition of many other ocular biometrics, acquisition of a periocular biometric does not demand a high user cooperation and a close capture distance [4].…”

Section: Introductionmentioning

confidence: 99%

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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Periocular biometrics: A survey

Cited by 32 publications

References 34 publications

Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network

Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network

A Privacy-Preserving Fully Homomorphic Encryption and Parallel Computation Based Biometric Data Matching

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

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