Image processing based eye detection methods a theoretical review

Vijayalaxmi, B.; Anuradha, Chavali; Sekaran, Kaushik; Meqdad, Maytham N.; Kadry, Seifedine

doi:10.11591/eei.v9i3.1783

Cited by 12 publications

(10 citation statements)

References 36 publications

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“…The experimentation is carried out on the maize crop which has a greater number of classifications of infections and diseases [21][22][23][24][25] due to bacteria, fungus, pests, and climatic conditions. The results of rot identification using faster regional convolutional neural network (R-CNN) are shown in Figure 7…”

Section: Resultsmentioning

confidence: 99%

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

Chandana

Ghantasala²,

Jeny

et al. 2020

IJECE

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The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.

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Section: Resultsmentioning

confidence: 99%

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

Chandana

Ghantasala²,

Jeny

et al. 2020

IJECE

Self Cite

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“…There is a distinct texture to human faces that can be used to distinguish them from other objects. A technique was established in [15] that detects face-like materials and infers the presence of a face. Textures are computed using second-order statistical characteristics (SGLD) [36] on 16 x 16 pixel sub-images.…”

Section: Texturementioning

confidence: 99%

“…Another Feature-Based face detection method is based on a skin color model that uses the distribution of chance in a color context. The face is detected in the image with a threshold on the modelled distribution as shown in [4] [15]. The second category algorithms are more robust, but they require greater machine effort.…”

Section: Introductionmentioning

confidence: 99%

A systematic review on Evaluation of Existing Face Eyes Detection Algorithms for Monitoring Systems

Sulaiman

Kocher²

2022

ACAD J NAWROZ UNIV

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Among the many issues facing the world, the issue of traffic accidents, many security facilities have developed research on fingerprints, palmistry biometrics and other biometrics. In modern technologies, facial and eye detection algorithms have been developed and identified. This paper includes comparisons of face and eye tracking methods, which are extremely useful for identifying individuals in a variety of situations. Facial biometrics are more stable due to their distinct features, which gives them an advantage over other biometrics such as palmistry and fingerprint. This work dealt with 74 studies of face and eye detection and a comparison of algorithms that were used in previous researches, where the Viola-jones algorithm was identified as the best facial algorithm in terms of accuracy up to 98% for different types of face databases because this algorithm depends on geometry of face and also able to detect all faces in the same image, Furthermore, it has been determined that SVM is the best eye detection algorithm due to the accuracy of its characteristics and the speed of execution in real time so, the highest accuracy achieved was 98% accurate .

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“…Three variables are found to contribute to distractions among drivers: a. Sleepiness or fatigue: sleep deprivation or exhaustion may occur throughout a journey. In this scenario, the driver would not be in the proper state of mind to continue driving [4]. b.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence system for driver distraction by stacked deep learning classification

Qibtiah

Zin²,

Hassan³

2023

Bulletin EEI

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Increasing efforts in the transportation system have recently improved driver safety and reduced crash rates. Lack of attention and fatigue directly affect the driver's consciousness. Driver distraction is an essential driver-specific factor in the practical applicability of forward collision warning (FCW). However, there are still too many false alarms generated by the existing FCW system to be mitigated. This paper proposes facial detection to identify features and test anomalies' prediction against drivers using stacked convolutional neural network (CNN) layers. The proposed model used overlapping HAAR and stacked CNN features to identify classifications of eye areas, such as open or closed. In addition to the sliding query window's overall intensity information. The conventional HAAR function, which elevates the brightness of nearby regions, is still preferable. This method considers current intelligent transportation system-based solutions to minimize distraction effects by continuously comparing with flexible thresholds. The experimental results are analyzed from accurate driving datasets. At 456 iterations, the results acquired over 80% accuracy, while loss is near zero. The implication of driver's risk tolerance is further explored in this manner. Several risks are connected to driving any type of transportation system.

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Image processing based eye detection methods a theoretical review

Cited by 12 publications

References 36 publications

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

A systematic review on Evaluation of Existing Face Eyes Detection Algorithms for Monitoring Systems

Artificial intelligence system for driver distraction by stacked deep learning classification

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