ORB Features and Isophotes Curvature Information for Eye Center Accurate Localization

Abstract

Pupil center recognition and location is an essential branch of ergonomics. It can be applied to emotion analysis and attention judgment. How to get the position of the pupil center from eye photos is the core of this field. Previous studies provided a helpful method, using scale-invariant feature transform (SIFT) to extract relevant features and combine them with the K-Nearest Neighbor (KNN) classifier. However, this method’s accuracy is not satisfying, and under some conditions, it will be position drift and other problems. We put forward a new idea to solve it by using Oriented FAST and Rotated BRIEF (ORB) features and Random Forest (RF) classifies. It is proved by experiment that our method improves the robustness of localization and the use of isophotes yields low computational cost, allowing for real-time processing. Meanwhile, we found that the ORB and RF are nearly as good, yielding an accuracy of 92.88% (BioID database).

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

“…Table 5 shows the accuracy, recall, precision, and F1score of the obtained results in the testing phase. The comparison between the proposed DBNN and [12,20,21,24] using the applied BioID dataset is shown in Fig. 15.…”

“…Furthermore, the proposed DBNN can be utilized using different standard datasets and any image format, even low-quality enrolled images. The comparative study of the proposed DBNN with the recent approaches [12,20,21,24] using the BioID dataset…”

“…Table 1 lists some of the intelligent techniques used for eye localization [18][19][20][21][22][23][24]. The authors used a Weighted Binarization Cascaded Convolutional Network (WBCCN) to localize the eye image with a minimum detection error rate of 0.66.…”

“…Moreover, Winston et al [23] utilized Convolutional Neural Networks (CNN) with a Support Vector Machine (SVM) for iris recognition based on the IIT Delhi iris dataset with 97.80% accuracy. In [24] presented an Eye Center Localization (ECL) approach applied to the BioID dataset with an accuracy of 92.88%. This work employed a combination of LBP and SURF methods to recognize and localize eye regions in facial images.…”

High precision visual localization method of UAV based on feature matching

“…The matching algorithms that use the Gaussian kernel function to construct scale space include the SIFT algorithm (Bellavia, 2022 ; Liu et al, 2022 ), SURF algorithm (Liu et al, 2019a ; Liu Z. et al, 2021 ; Fatma et al, 2022 ), and ORB algorithm (Liu et al, 2019b ; Chen et al, 2022 ; Xie et al, 2022 ; Xue et al, 2022 ), etc. This kind of algorithm has good robustness and fast matching speed, but the Gaussian kernel convolution operation will lead to the loss of edge information of the image, which seriously affects the stability of feature points and descriptors.…”