Person identification at a distance via ocular biometrics

Jain, Arpit; Mittal, Paritosh; Goswami, Gaurav; Vatsa, Mayank; Singh, Richa

doi:10.1109/isba.2015.7126353

Cited by 9 publications

(2 citation statements)

References 23 publications

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“…The iris recognition system performance is relying upon between the distance of sensor and the subject. This condition has caused difficulty in verifying a person due to low quality of iris data [5] caused by several noise factors such as reflections [6][7][8][9][10], motion-blurred [11], off-angle [12], low lighting [13,14] and occlusion of eyelid [15][16][17]. Besides, the performance of iris segmentation in this environment is negatively impacted when the images resolution is low [3,4].…”

Section: Introductionmentioning

confidence: 99%

Fusion Iris and Periocular Recognitions in Non-Cooperative Environment

Raffei

Sutikno

Asmuni

et al. 2019

IJEEI

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The performance of iris recognition in non-cooperative environment can be negatively impacted when the resolution of the iris images is low which results in failure to determine the eye center, limbic and pupillary boundary of the iris segmentation. Hence, a combination with periocular features is suggested to increase the authenticity of the recognition system. However, the texture feature of periocular can be easily affected by a background complication while the colour feature of periocular is still limited to spatial information and quantization effects. This happens due to different distances between the sensor and the subject during the iris acquisition stage as well as image size and orientation. The proposed method of periocular feature extraction consists of a combination of rotation invariant uniform local binary pattern to select the texture features and a method of color moment to select the color features. Besides, a hue-saturation-value channel is selected to avoid loss of discriminative information in the eye image. The proposed method which consists of combination between texture and colour features provides the highest accuracy for the periocular recognition with more than 71.5% for the UBIRIS.v2 dataset and 85.7% for the UBIPr dataset. For the fusion recognitions, the proposed method achieved the highest accuracy with more than 85.9% for the UBIRIS.v2 dataset and 89.7% for the UBIPr dataset.

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Section: Introductionmentioning

confidence: 99%

Fusion Iris and Periocular Recognitions in Non-Cooperative Environment

Raffei

Sutikno

Asmuni

et al. 2019

IJEEI

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show abstract

“…Research in visible spectrum iris recognition [18,32,38] has witnessed significant growth in recent years and is being actively explored. Iris recognition in the visible spectrum can be utilized for applications such as authentication in outdoor environments where near-infrared iris images may be difficult to acquire.…”

Section: Iris Presentation Attack Detection Performance Of the Proposedmentioning

confidence: 99%

Textured Contact Lens based Iris Presentation Attack in Uncontrolled Environment

Yadav¹

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Textured Contact Lens Based Iris Presentation Attack In Uncontrolled Environment Daksha Yadav The widespread use of smartphones has spurred the research in mobile iris devices. Due to their convenience, these mobile devices are also utilized in unconstrained outdoor conditions. At the same time, iris recognition in the visible spectrum has developed into an active area of research. These scenarios have necessitated the development of reliable iris recognition algorithms for such an uncontrolled environment. Additionally, iris presentation attacks such as textured contact lens pose a major challenge to current iris recognition systems. Motivated by these factors, in this thesis, a detailed analysis of the effect of textured contact lenses on iris recognition in an uncontrolled environment is presented by creating Mobile Uncontrolled Iris Presentation Attack Database. It consists of more than 10,000 iris images of subjects wearing textured contact lens and without wearing contact lenses captured in an indoor and outdoor environment using a mobile iris sensor. The first contact lens database in the visible spectrum is also introduced, named as UVCLI database. Using this database, a detailed analysis of the effect of textured contact lenses on iris recognition in the visible spectrum is presented. It is observed that textured contact lenses degrade the unconstrained iris recognition performance by over 25% and thus, may be utilized intentionally or unintentionally to attack existing iris recognition systems. Also, the first study to investigate the impact of textured contact lenses on identity impersonation is performed. The results demonstrate that a perpetrator can impersonate an enrolled subject by wearing the same textured contact lens. This thesis also presents a novel algorithm, DensePAD, which utilizes DenseNet based convolutional neural network architecture for the near-infrared spectrum based iris presentation attack detection. The proposed algorithm is trained on a combined iris database of more than 270, 000 real and attack iris images. In-depth experimental evaluation of this algorithm reveals its superior performance in detecting iris presentation attack images on the combined iris database as well as the proposed MUIPAD.

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Periocular biometrics and its relevance to partially masked faces: A survey

Sharma

Ross²

2023

Computer Vision and Image Understanding

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Person identification at a distance via ocular biometrics

Cited by 9 publications

References 23 publications

Fusion Iris and Periocular Recognitions in Non-Cooperative Environment

Fusion Iris and Periocular Recognitions in Non-Cooperative Environment

Textured Contact Lens based Iris Presentation Attack in Uncontrolled Environment

Periocular biometrics and its relevance to partially masked faces: A survey

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