FRCNN-GNB: Cascade Faster R-CNN With Gabor Filters and Naïve Bayes for Enhanced Eye Detection

Nsaif, Ahmed Khudhur; Ali, Sawal Hamid Md; Jassim, Khider Nassif; Nseaf, Asama Kuder; Sulaiman, Riza; Al-Qaraghuli, Ammar; Wahdan, Omar M.; Nayan, Nazrul Anuar

doi:10.1109/access.2021.3052851

Cited by 23 publications

(17 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, a fully connected layer outputs the object class and bounding box of the ROIs. Nasaif et al [19] proposed FRCNN-GNB for eye detection. They use Faster R-CNN [16] to detect initial eye regions and then apply Gabor filters and a naïve Bayes model for the final eye detection.…”

Section: Related Workmentioning

confidence: 99%

Fast Eye Detector Using Siamese Network for NIR Partial Face Images

Ogino¹,

Shoji²,

Toizumi³

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper proposes a fast eye detection method based on fully-convolutional Siamese networks for iris recognition. The iris on the move system requires to capture high resolution iris images from a moving subject for iris recognition. Therefore, capturing images contains both eyes at high-frame-rate increases the chance of iris imaging. In order to output the authentication result in real time, the system requires a fast eye detector extracting the left and right eye regions from the image. Our method extracts features of a partial face image and a reference eye image using Siamese network frameworks. Similarity heat maps of both eyes are created by calculating the spatial cosine similarity between extracted features. Besides, we use CosFace as a loss function for training to discriminate the left and right eyes with high accuracy even with a shallow network. Experimental results show that our method trained by CosFace is fast and accurate compared with conventional generic object detection methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Fast Eye Detector Using Siamese Network for NIR Partial Face Images

Ogino¹,

Shoji²,

Toizumi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The population size considered was 10 and the maximum iteration count considered was 25. Here, the proposed PO-FRCNN was compared with several optimization algorithms such as GWO [39], BMO [40], BOA [41], and IRDA [35]and machine learning models such as NN [43], CNN [44], NN+CNN [43] [44] and FRCNN [37]. The bullae index method [42] was also gone through for emphysema classification.…”

Section: A Simulation Set-upmentioning

confidence: 99%

“…(e) FPR: It is, "the ratio of count of false positive predictions to the entire count of negative predictions" as in Eq (37)(37).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Local Ternary Pattern on Parameter Optimized-Faster Region Convolutional Neural Network for Pulmonary Emphysema Diagnosis

2021

View full text Add to dashboard Cite

Emphysema is a lung disease that occurs due to abnormal alveoli expansion. This chronic disease causes difficulty in breathing which can lead to lung cancer. The progressive destruction of emphysema can be assessed by Computed Tomography (CT) scans and pulmonary function tests. The severity of the disease may extend to a stage where one can risk their life emphasizing the early detection of emphysema. Primary diagnosis can be done using spirometry and CT for early detection of the disease reducing the mortality rates. Difficulties associated with different diagnostic procedures and inter and intraobserver variations have made blooming researches on more computer-aided techniques. This paper intends to develop a computer-aided technique using the improved deep learning strategy. The initial process is image pre-processing, which is performed by histogram equalization and median filtering. Further, the Fuzzy C Means (FCM) clustering is used for segmentation. After segmentation, a new Adaptive Local Ternary Pattern (ALTP) is used for extracting the pattern descriptor, which is further utilized for classification. As a new contribution, the Parameter Optimized-Faster Region Convolutional Neural Network (PO-FRCNN) is developed for performing the diagnosis. The enhancement of pattern formation and deep classification is accomplished by the Improved Red Deer Algorithm (IRDA), which helps to tune the significant parameters that have a positive influence on the accurateness. The benchmark and real-time dataset are used for performing the experimentation. The results show that the proposed method yields the best result and can effectively diagnose emphysema when compared to state-of-the-art techniques.INDEX TERMS Adaptive local ternary pattern, fuzzy C means clustering, modified deep learning, parameter optimized-faster region convolutional neural network, pulmonary emphysema.

show abstract

“…For outlier detection in the industrial internet of things (IIoT) system, GNB is used as mentioned in [28]. Also, in [29], [30], GNB is implemented in such detection problems.…”

Section: Gnbmentioning

confidence: 99%

An integrated machine learning model for indoor network optimization to maximize coverage

Reza

Rifat²,

Ahmed³

2021

IJEECS

View full text Add to dashboard Cite

Indoor network optimization is not a simple task due to the obstacles, interference, and attenuation of the signal in an environment. Intense noises can affect the intelligibility of the signal and reduce the coverage strength significantly which results in a poor user experience. Most of the existing works are associated with finding the location of the devices via different mathematical and generic algorithmic approaches, but very few are focused on implying machine learning algorithms. The purpose of this research is to introduce an integrated machine learning model to find maximum indoor coverage with a minimum number of transmitters. The users in the indoor environment also have been allocated based on the most reliable signal strength and the system is also capable of allocating new users. K-means clustering, K-nearest neighbor (KNN), support vector machine (SVM), and Gaussian Naïve Bayes (GNB) have been used to provide an optimized solution. It is found that KNN, SVM, and GNB obtained maximum accuracy of 100% in some cases. However, among all the algorithms, KNN performed the best and provided an average accuracy of 93.33%. K-fold cross-validation (Kf-CV) technique has been added to validate the experimental simulations and re-evaluate the outcomes of the machine learning models.

show abstract

FRCNN-GNB: Cascade Faster R-CNN With Gabor Filters and Naïve Bayes for Enhanced Eye Detection

Cited by 23 publications

References 37 publications

Fast Eye Detector Using Siamese Network for NIR Partial Face Images

Fast Eye Detector Using Siamese Network for NIR Partial Face Images

Adaptive Local Ternary Pattern on Parameter Optimized-Faster Region Convolutional Neural Network for Pulmonary Emphysema Diagnosis

An integrated machine learning model for indoor network optimization to maximize coverage

Contact Info

Product

Resources

About