An integrated approach for eye centre localization using deep networks and rectangular-intensity-gradient technique

Ahmad, Naseem; Yadav, Kuldeep; Ahmed, Manir; Laskar, Rabul Hussain; Hossain, Ashraf

doi:10.1016/j.jksuci.2022.02.015

Cited by 7 publications

(8 citation statements)

References 34 publications

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“…Ahmad et al [14] suggested Faster Recurrent Convolutional Neural Networks (FRCNN), AlexNet, and a Rectangular-Intensity-Gradient (RIG) technique as a unified method for face recognition, eye recognition, detection of eye openness, and eye center localization. Both eyes were identified using Faster RCNN, then, AlexNet assisted in determining the eye's status (closed or open).…”

Section: Related Workmentioning

confidence: 99%

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

Tarek¹,

Shohieb²,

Elhady³

et al. 2023

Computer Systems Science and Engineering

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The ability to detect and localize the human eye is critical for use in security applications and human identification and verification systems. This is because eye recognition algorithms have multiple challenges, such as multi-pose variations, ocular parts, and illumination. Moreover, the modern security applications fail to detect facial expressions from eye images. In this paper, a Speeded-Up Roust Feature (SURF) Algorithm was utilized to localize the face images of the enrolled subjects. We highlighted on eye and pupil parts to be detected based on SURF, Hough Circle Transform (HCT), and Local Binary Pattern (LBP). Afterward, Deep Belief Neural Networks (DBNN) were used to classify the input features results from the SURF algorithm. We further determined the correctly and wrongly classified subjects using a confusion matrix with two class labels to classify people whose eye images are correctly detected. We apply Stochastic Gradient Descent (SGD) optimizer to address the overfitting problem, and the hyper-parameters are fine-tuned based on the applied DBNN. The accuracy of the proposed system is determined based on SURF, LBP, and DBNN classifier achieved 95.54% for the ORL dataset, 94.07% for the BioID, and 96.20% for the CASIA-V5 dataset. The proposed approach is more reliable and more advanced when compared with state-of-the-art algorithms.

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Section: Related Workmentioning

confidence: 99%

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

Tarek¹,

Shohieb²,

Elhady³

et al. 2023

Computer Systems Science and Engineering

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“…These handcrafted features failed to detect eyes in an uncontrolled environment. Ahmad et al (2022) proposed a Faster RCNN approach for eye detection. They were using deep features to detect eyes.…”

Section: Eye Detection Using Yolov3mentioning

confidence: 99%

“…Hsu and Chung (2021) used an iris ripple filter with gradient estimation for iris centre localization. Ahmad et al (2022) used a Faster RCNN for eye detection and a rectangular intensity‐gradient method for iris centre localization. They used face detection before eye detection, and their method failed due to the glass frame, heavy occlusion, and hairs falling on the eyes.…”

Section: Related Workmentioning

confidence: 99%

“…The eyes are the most stable and salient feature among the other facial features. Many biometric and computer vision applications need iris centre information for gaze estimation, iris recognition, driver drowsiness detection, liveness detection, and so forth (Ahmad et al, 2022; Ahmed & Laskar, 2019, 2021, 2022; Hsu & Chung, 2021; Poulopoulos & Psarakis, 2022). However, pose variation, scale variation, occlusion, image quality, and specular reflections are some challenges that make iris centre localization difficult.…”

Section: Introductionmentioning

confidence: 99%

“…Utilizing these intrusive characteristics, most researchers localize the iris centre using handcrafted features. Therefore, circle fitting techniques such as circular HOG, circular geometric algebra (CGA) (Ma et al, 2020), ISF, rectangular‐intensity‐gradient (RIG) (Ahmad et al, 2022), and so forth, are used for iris detection. Gradient‐based methods use dot products between gradient and displacement vectors for iris centre localization.…”

Section: Introductionmentioning

confidence: 99%

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A cascaded deep learning framework for iris centre localization in facial image

Ahmad,

Muhammad,

Yadav

et al. 2023

Expert Systems

Self Cite

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Accurate iris centre localization is crucial in many computer vision and facial biometric applications such as gaze estimation, human–computer interaction, iris recognition, and liveness detection. However, it is challenging in an uncontrolled environment due to variations like pose, scale, rotation, specular reflection, and image quality. Therefore, a cascaded deep learning framework for iris centre localization in facial images is proposed that is robust to the abovementioned variations. The proposed approach consists of (i) YOLOv3 for eye detection, (ii) UNet for iris segmentation, and (iii) statistical modelling for iris centre localization. The eyes are first detected using the YOLOv3, and subsequently, iris segmentation is performed within the detected eyes using the UNet. Following iris segmentation, statistical modelling is employed to enhance the localization accuracy of the iris centre. Experiments were performed on benchmark databases, resulting in a standardized error measure SED of 3.405 pixels for BioID and 3.259 pixels for GI4E databases. In addition, the robustness of the proposed eye detection model was further evaluated on the Yale B for illumination variations and the CAS‐PEAL for pose variations.

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Eye Centre Localisation with Convolutional Neural Networks in High- and Low-Resolution Images

Zhang

2022

Computational Science and Its Applications – ICCSA 2022

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An integrated approach for eye centre localization using deep networks and rectangular-intensity-gradient technique

Cited by 7 publications

References 34 publications

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

A cascaded deep learning framework for iris centre localization in facial image

Eye Centre Localisation with Convolutional Neural Networks in High- and Low-Resolution Images

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