Deep-Learning Based Joint Iris and Sclera Recognition with YOLO Network for Identity Identification

Chuang, Chia-Wei; Fan, Chih‐Peng

doi:10.12720/jait.12.1.60-65

Cited by 28 publications

(6 citation statements)

References 13 publications

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“…Circular hough transform is used to detect the iris boundary and the pupil boundary. The output of this gives the center coordinates of the iris and the pupil [3], [17], [18]. It also gives the radius of the iris and the pupil.…”

Section: Circular Hough Transformmentioning

confidence: 99%

Sclera boundary localization using circular hough transform and a modified run-data based algorithm

Taiwo Adeniyi,

Tinuke Omolewa,

Kehinde Adeniyi

2024

TELKOMNIKA

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Security challenges over the years has led to the need for an improvement in the traditional security approaches. This led to the advent of biometrics. Recently, among the biometric approaches, sclera has been an area of imense study. This is due to its accuracy; however, segmentation of the sclera has been a limiting factor to the application of this biometric trait. Several approaches have been proposed in literature but there is still the need to improve the segmentation accuracy. This study proposes the use of circular hough transform and a modified run-data based algorithm. The study also presented a sclera recognition system using the compound local binary pattern for features extraction and Manhattan distance for classification. The system produced a segmentation accuracy of 99.9% for sclera blood vessels, periocular and iris (SBVPI) sclera database and 100% for manually captured sclera database. The system produced an accuracy of 99.98 for SBVPI sclera database and 99.99% for manually captured sclera database.

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Section: Circular Hough Transformmentioning

confidence: 99%

Sclera boundary localization using circular hough transform and a modified run-data based algorithm

Taiwo Adeniyi,

Tinuke Omolewa,

Kehinde Adeniyi

2024

TELKOMNIKA

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“…In the localization task of IR, the YOLO model has a crucial role in the localization of iris [156][157][158]. It is mainly due to the advantages of the YOLO model, such as fast speed and high generalization ability.…”

Section: Iris Localizationmentioning

confidence: 99%

Deep Learning for Iris Recognition: A Review

Yin¹,

He²,

Zhang³

et al. 2023

Preprint

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Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning.

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“…Fig. 6 shows the YOLO model which can predict bounding boxes with confidence scores and class probabilities directly by also dividing the image into a grid [17,37]. Table III shows the hyperparameters used in the YOLOv7 implementation.…”

Section: A Vehicle Detection Using Instance Segmentationmentioning

confidence: 99%

Observation-Centric with Appearance Metric for Computer Vision-Based Vehicle Counting

Brillantes,

Sybingco,

Billones

et al. 2023

JAIT

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Tracking objects in video sequences is a key step for applications involving computer vision like traffic monitoring and security systems. Occlusion is a frequent problem in object tracking which can result in the tracker losing track of the occluded object or misidentifying it with the occluding object. Moreover, the limited memory and computing power of traffic analysis systems presents a scaling problem, especially in object tracking applications. This paper aims to improve object tracking performance by minimizing data association errors in low frame rate tracking applications. Reducing frame rates alleviates memory and computing power limitations, and utilizing a tracker that can handle occlusion can address occlusion-related issues in object tracking. The proposed tracking method, Mask-OCSORT, uses the observation-tracking method with cosine similarity, intersection-over-union, and velocity consistency metrics for the association problem. The paper analyzes the effect of using bounding box and mask predictions of deep learning models in generating tracks. This study uses evaluation metrics like HOTA, MOTA, and IDF1 to assess the proposed tracking method, and employs evaluation metrics such as precision, recall, and F-score to assess the counting based on generated IDs from the tracking method. The study applied the Mask-OCSORT tracking for vehicle counting application and achieved an F-score of 87.18% at 5 frames per second (fps), and 75% at 1 fps.

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Deep-Learning Based Joint Iris and Sclera Recognition with YOLO Network for Identity Identification

Cited by 28 publications

References 13 publications

Sclera boundary localization using circular hough transform and a modified run-data based algorithm

Sclera boundary localization using circular hough transform and a modified run-data based algorithm

Deep Learning for Iris Recognition: A Review

Observation-Centric with Appearance Metric for Computer Vision-Based Vehicle Counting

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