Accurate and robust eye center localization via fully convolutional networks

Xia, Yifan; Yu, Hui; Wang, Fei‐Yue

doi:10.1109/jas.2019.1911684

Cited by 60 publications

(28 citation statements)

References 66 publications

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“…Ensemble of random forest is also used in pupil localization [3]. Recently, fully convolutional network is adopted in eye center localization, which has a good localization accuracy for the frontal faces images on BioID and GI4E databases [4], [5]. However, in the appearance-based method, a large number of training samples are often required, and training data under certain conditions are usually adopted.…”

Section: B Iris Center Localization Methodsmentioning

confidence: 99%

Iris Center Localization Using Energy Map With Image Inpaint Technology and Post-Processing Correction

Dai

Liu

et al. 2020

IEEE Access

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Iris center localization is the basis of iris biometrics, face recognition and gaze tracking. However, individual differences, changes in facial expression, varying light conditions, occlusion, and so on, all bring great challenges to accurately localize the iris center. In order to improve localization accuracy in low-quality images and meet the need of efficiency in practical applications, a novel method of iris center localization is proposed in this paper using energy map synthesis based on image gradient, image inpaint technology, and post-processing correction. The image inpaint technology is firstly adopted to inhibit the effect of some specular reflection. Then the energy maps based on image gradient and eye ROI (Region Of Interest) midpoint are synthesized to significantly improve the localization accuracy. In the end, post-processing correction is carried out to eliminate influence of the closed eye and other large derivations to further improve the localization accuracy. The algorithm is verified on the challenging BioID database, Talking Face Video database and the MUCT face database. The result shows the localization accuracy has outperformed the state-of-the-art unsupervised methods on the three databases, and it is suitable for real-time applications. INDEX TERMS Iris center localization, image gradient, image inpaint, energy map synthesis, post-processing correction.

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Section: B Iris Center Localization Methodsmentioning

confidence: 99%

Iris Center Localization Using Energy Map With Image Inpaint Technology and Post-Processing Correction

Dai

Liu

et al. 2020

IEEE Access

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“…DL is an approach to Artificial Intelligence (AI), that achieves great power by representing data as a nested hierarchy of concepts, starting from simpler concepts to more abstract representations [18] [19]. DL has been successfully employed in several applications, such us in cyber security [20] [21], in neuro-science [22] [23] [24], in sentiment classification [25] [26], image decomposition [27] and fault detection systems [28]. DL has also been proposed to deal with the problem of automatic detection of surface anomalies.…”

Section: Related Workmentioning

confidence: 99%

A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers

Ieracitano

Paviglianiti

Campolo

et al. 2021

IEEE/CAA J. Autom. Sinica

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The manufacturing of nanomaterials by an electrospinning process requires accurate and meticulous inspection of Scanning Electron Microscope (SEM) images of the electrospun nanofiber, to ensure no structural defects are produced. The possible presence of anomalies is known to make the nanofibrous material useless in the practical application of any nanotechnology. Hence, automatic monitoring and quality control of nanomaterials has become an important challenge in the context of Industry 4.0. In this paper, we propose a novel automatic classification system for homogenous (anomaly-free) and nonhomogenous (with defects) nanofibers avoiding the processing of the redundant full SEM image. Specifically, the image to be analyzed is partitioned into sub-images (nanopatches) that are then used as input to a hybrid unsupervised and supervised machine learning system. An Autoencoder (AE) is first trained with unsupervised learning to generate a code representing the input image with a number of relevant features. Next, a Multilayer Perceptron (MLP), trained with supervised learning, uses the extracted features to classify non-homogenous nanofiber (NH-NF) and homogenous nanofiber (H-NF) materials. The resulting novel AE-MLP system is shown to outperform other standard machine learning models and recent state-of-the-art techniques, reporting accuracy rates up to 92.5%. In addition, our proposed approach achieves significant model complexity reduction with respect to other deep learning strategies such as Convolutional Neural Networks (CNN). The promising performance achieved in this benchmark study will stimulate the application of our proposed framework in a range of challenging industrial manufacturing tasks.

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“…However, because this method still depends on a heavy end-to-end convolutional neural network (CNN) model, it is difficult to interpret the model and requires a greater reduction in the number of operations for on-device use in real-time. Xia et al [ 16 ] adapted a fully convolutional neural network (CNN) into a shallow structure with a large kernel convolutional block and transferred the performance from semantic segmentation to an eye center localization task through fine-tuning. Other methods have recently attempted to apply a generative adversarial network (GAN) to gaze perception.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Pupil Tracking Based on Rule Distillation of Cascade Regression Forest

Kim

Jeong

2020

Sensors

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As the demand for human-friendly computing increases, research on pupil tracking to facilitate human–machine interactions (HCIs) is being actively conducted. Several successful pupil tracking approaches have been developed using images and a deep neural network (DNN). However, common DNN-based methods not only require tremendous computing power and energy consumption for learning and prediction; they also have a demerit in that an interpretation is impossible because a black-box model with an unknown prediction process is applied. In this study, we propose a lightweight pupil tracking algorithm for on-device machine learning (ML) using a fast and accurate cascade deep regression forest (RF) instead of a DNN. Pupil estimation is applied in a coarse-to-fine manner in a layer-by-layer RF structure, and each RF is simplified using the proposed rule distillation algorithm for removing unimportant rules constituting the RF. The goal of the proposed algorithm is to produce a more transparent and adoptable model for application to on-device ML systems, while maintaining a precise pupil tracking performance. Our proposed method experimentally achieves an outstanding speed, a reduction in the number of parameters, and a better pupil tracking performance compared to several other state-of-the-art methods using only a CPU.

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Accurate and robust eye center localization via fully convolutional networks

Cited by 60 publications

References 66 publications

Iris Center Localization Using Energy Map With Image Inpaint Technology and Post-Processing Correction

Iris Center Localization Using Energy Map With Image Inpaint Technology and Post-Processing Correction

A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers

Energy Efficient Pupil Tracking Based on Rule Distillation of Cascade Regression Forest

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