Dataset for the evaluation of eye detector for gaze estimation

Ponz, Victoria; Villanueva, Arantxa; Cabeza, Rafael

doi:10.1145/2370216.2370364

Cited by 18 publications

(9 citation statements)

References 5 publications

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“…2) Experiments in BioID and Gi4E Database: A comparison of the proposed method with the state of the art methods is carried out for BioID [43] and Gi4E [44] databases. The BioID database consists of images of 23 individuals taken at different times of the day.…”

Section: ) Calibrationmentioning

confidence: 99%

Fast and accurate algorithm for eye localisation for gaze tracking in low‐resolution images

George

Routray

2016

IET Computer Vision

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Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.

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Section: ) Calibrationmentioning

confidence: 99%

Fast and accurate algorithm for eye localisation for gaze tracking in low‐resolution images

George

Routray

2016

IET Computer Vision

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“…To maintain the reliability in our performance measure, we utilised different metrics as well as the newly introduced S ED in this work. Table 2 summarises the results achieved from the proposed approach using wec and bec metrics, which have been used in recent similar works [35][36][37][42][43][44]. We are specifically interested in the wec measure when error ≤0.05, which indicates the model estimation within the pupil diameter (i.e., more restricted).…”

Section: Resultsmentioning

confidence: 99%

“…Table 2 summarises the results achieved from the proposed approach using wec and bec metrics, which have been used in recent similar works [ 35 , 36 , 37 , 42 , 43 , 44 ]. We are specifically interested in the wec measure when error ≤0.05, which indicates the model estimation within the pupil diameter (i.e., more restricted).…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, we evaluated our method on a comparatively larger dataset known as GI4E [43] containing more diversity involving various morphology types (e.g., eye size, eye/iris features, gender, ethnicity, varying backgrounds and illuminations). It should be noted that despite higher resolution images (800 × 600 pixels), the size of the eye-frame rectangles is comparatively small.…”

Section: Datasetsmentioning

confidence: 99%

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Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

Khan

Hussain

Kuru

et al. 2020

Sensors

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Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye frames within the input image. Then, we use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along with a standardised distance metric that we introduce for the first time in this study. The proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare, and automated deception detection.

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“…For our experiments we use the UULM [34] and Gi4E [27] publicly available datasets. Additionally, we constructed our own database for the purposes of the paper.…”

Section: Annotated Datamentioning

confidence: 99%