Automated Pupillometer Using Edge Detection in OpenCV for Pupil Size and Reactivity Assessment

Garcia, Ramon G.; Avendano, Glenn O.; Agdeppa, Denzel Blake F.; Castillo, Ken J.; Go, Neil Roger Sd.; Mesina, Mikhail A.

doi:10.1109/icispc.2019.8935852

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…In the recommended model, the mean error of the length and area are 5.437% and 10.57%, respectively. Besides, the comparison with previous research [ 16 ] is shown in Table 4 .…”

Section: Discussionmentioning

confidence: 89%

“…In clinical practice, the size of the pupil is predicted by the experience of the medical staff. The behavior will not be unified since it depends on the subjective consciousness or the degree of fatigue among medical staff [ 16 ]. Medical equipment for pupil measurement is expensive and must rely on the cooperation of patients, which is inconvenient for medical staff.…”

Section: Introductionmentioning

confidence: 99%

“…There are few studies on pupil size. Concerning the study of pupil size, for example, Garcia et al, used OpenCV to preprocess an image and then used the Otsu threshold and contour detection to complete pupil size detection [ 16 ]. De Souza et al, proposed using two complementary independent algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pupil Size Prediction Techniques Based on Convolution Neural Network

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et al. 2021

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The size of one’s pupil can indicate one’s physical condition and mental state. When we search related papers about AI and the pupil, most studies focused on eye-tracking. This paper proposes an algorithm that can calculate pupil size based on a convolution neural network (CNN). Usually, the shape of the pupil is not round, and 50% of pupils can be calculated using ellipses as the best fitting shapes. This paper uses the major and minor axes of an ellipse to represent the size of pupils and uses the two parameters as the output of the network. Regarding the input of the network, the dataset is in video format (continuous frames). Taking each frame from the videos and using these to train the CNN model may cause overfitting since the images are too similar. This study used data augmentation and calculated the structural similarity to ensure that the images had a certain degree of difference to avoid this problem. For optimizing the network structure, this study compared the mean error with changes in the depth of the network and the field of view (FOV) of the convolution filter. The result shows that both deepening the network and widening the FOV of the convolution filter can reduce the mean error. According to the results, the mean error of the pupil length is 5.437% and the pupil area is 10.57%. It can operate in low-cost mobile embedded systems at 35 frames per second, demonstrating that low-cost designs can be used for pupil size prediction.

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“…In the recommended model, the mean error of the length and area are 5.437% and 10.57%, respectively. Besides, the comparison with previous research [ 16 ] is shown in Table 4 .…”

Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%