Real-Time Detection and Measurement of Eye Features from Color Images

Muller muscle conjunctival resection or external levator advancement; a quantitative comparison of symmetry in unilateral ptosis

PURPOSE

To compare muller muscle conjunctival resection (MMCR) and external levator advancement (ELA) in terms of objective and subjective symmetry in unilateral ptosis.

METHODS

The patients who underwent unilateral MMCR (group1, n = 25) and ELA (group2, n = 25) were reviewed retrospectively. With the written algorithm, margin reflex distance-1 (MRD1), localization of the contour peak, and the percentage of overlapping curvatures (POC) of both upper eyelid curves were calculated. Semi-automated measurements were compared with the manual measurements. The correlation between objective and subjective symmetry was evaluated.

RESULTS

Preoperative levator functions were similar in both groups (13.93 ± 3.25 mm and 13.3 ± 2.86 mm, respectively). We found strong correlations between manual and semi-automated measurements (ICC: 0.942, 95% CI = 0.924-0.956, p < 0.001). Preoperative MRD1 (2.15 ± 0.90 vs 1.51 ± 1.01, p = 0.022) and POC was lower in group 2 (66% vs 47.2%, p = 0.01). In the postoperative period, the increase in MRD1 and POC were similar in both groups (p = 0.2 and p = 0.7 respectively), and the contour peak displaced temporally (p < 0.01) providing a symmetric peak in both groups. Subjective symmetry increased as the difference in MRD1 between two eyes decreased (r = -0.456, p = 0.001), and POC increased (r = 0.396, p = 0.004).

CONCLUSIONS

Besides subjective symmetry, MMCR and ELA are equally effective in ensuring symmetry objectively, as measured with MRD1 and contour peak symmetry and POC in unilateral ptosis. POC may be used as a criterion of symmetry in the evaluation of ptosis.

Integrated Multi-Model Face Shape and Eye Attributes Identification for Hair Style and Eyelashes Recommendation

Identifying human face shape and eye attributes is the first and most vital process before applying for the right hairstyle and eyelashes extension. The aim of this research work includes the development of a decision support program to constitute an aid system that analyses eye and face features automatically based on the image taken from a user. The system suggests a suitable recommendation of eyelashes type and hairstyle based on the automatic reported users’ eye and face features. To achieve the aim, we develop a multi-model system comprising three separate models; each model targeted a different task, including; face shape classification, eye attribute identification and gender detection model. Face shape classification system has been designed based on the development of a hybrid framework of handcrafting and learned feature. Eye attributes have been identified by exploiting the geometrical eye measurements using the detected eye landmarks. Gender identification system has been realised and designed by implementing a deep learning-based approach. The outputs of three developed models are merged to design a decision support system for haircut and eyelash extension recommendation. The obtained detection results demonstrate that the proposed method effectively identifies the face shape and eye attributes. Developing such computer-aided systems is suitable and beneficial for the user and would be beneficial to the beauty industrial.

An image processing pipeline to segment iris for unconstrained cow identification system

One of the most evident costs in cow farming is the identification of the animals. Classic identification processes are labour-intensive, prone to human errors and invasive for the animal. An automated alternative is an animal identification based on unique biometric patterns like iris recognition; in this context, correct segmentation of the region of interest becomes of critical importance. This work introduces a bovine iris segmentation pipeline that processes images taken in the wild, extracting the iris region. The solution deals with images taken with a regular visible-light camera in real scenarios, where reflections in the iris and camera flash introduce a high level of noise that makes the segmentation procedure challenging. Traditional segmentation techniques for the human iris are not applicable given the nature of the bovine eye; at this aim, a dataset composed of catalogued images and manually labelled ground truth data of Aberdeen-Angus has been used for the experiments and made publicly available. The unique ID number for each different animal in the dataset is provided, making it suitable for recognition tasks. Segmentation results have been validated with our dataset showing high reliability: with the most pessimistic metric (i.e. intersection over union), a mean score of 0.8957 has been obtained.

Automated Vision-Based High Intraocular Pressure Detection Using Frontal Eye Images

Glaucoma, the silent thief of vision, is mostly caused by the gradual increase of pressure in the eye which is known as intraocular pressure (IOP). An effective way to prevent the rise in eye pressure is by early detection. Prior computer vision-based work regarding IOP relies on fundus images of the optic nerves. This paper provides a novel vision-based framework to help in the initial IOP screening using only frontal eye images. The framework first introduces the utilization of a fully convolutional neural (FCN) network on frontal eye images for sclera and iris segmentation. Using these extracted areas, six features that include mean redness level of the sclera, red area percentage, Pupil/Iris diameter ratio, and three sclera contour features (distance, area, and angle) are computed. A database of images from the Princess Basma Hospital is used in this work, containing 400 facial images; 200 cases with normal IOP; and 200 cases with high IOP. Once the features are extracted, two classifiers (support vector machine and decision tree) are applied to obtain the status of the patients in terms of IOP (normal or high). The overall accuracy of the proposed framework is over 97.75% using the decision tree. The novelties and contributions of this work include introducing a fully convolutional network architecture for eye sclera segmentation, in addition to scientifically correlating the frontal eye view (image) with IOP by introducing new sclera contour features that have not been previously introduced in the literature from frontal eye images for IOP status determination.

Etracker: A Mobile Gaze-Tracking System with Near-Eye Display Based on a Combined Gaze-Tracking Algorithm

Eye tracking technology has become increasingly important for psychological analysis, medical diagnosis, driver assistance systems, and many other applications. Various gaze-tracking models have been established by previous researchers. However, there is currently no near-eye display system with accurate gaze-tracking performance and a convenient user experience. In this paper, we constructed a complete prototype of the mobile gaze-tracking system 'Etracker' with a near-eye viewing device for human gaze tracking. We proposed a combined gaze-tracking algorithm. In this algorithm, the convolutional neural network is used to remove blinking images and predict coarse gaze position, and then a geometric model is defined for accurate human gaze tracking. Moreover, we proposed using the mean value of gazes to resolve pupil center changes caused by nystagmus in calibration algorithms, so that an individual user only needs to calibrate it the first time, which makes our system more convenient. The experiments on gaze data from 26 participants show that the eye center detection accuracy is 98% and Etracker can provide an average gaze accuracy of 0.53° at a rate of 30⁻60 Hz.