Random neural networks for dimensionality reduction and regularized supervised learning

Hu, Renjie

doi:10.17077/etd.d47y-9s7b

Cited by 1 publication

(1 citation statement)

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“…At present, the algorithms for recognizing the open and closed state of eyes are divided into two types: manual feature extraction and automatic feature extraction. Among them, manual feature extraction mainly includes the template matching detection method, texture feature detection method, and shape feature detection method [26,27]. These methods rely on the extraction of texture features, and the selection of texture features requires a great deal of experimentation and sufficient experience.…”

Section: Methodsmentioning

confidence: 99%

Controller Fatigue State Detection Based on ES-DFNN

et al. 2021

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The fatiguing work of air traffic controllers inevitably threatens air traffic safety. Determining whether eyes are in an open or closed state is currently the main method for detecting fatigue in air traffic controllers. Here, an eye state recognition model based on deep-fusion neural networks is proposed for determination of the fatigue state of controllers. This method uses transfer learning strategies to pre-train deep neural networks and deep convolutional neural networks and performs network fusion at the decision-making layer. The fused network demonstrated an improved ability to classify the target domain dataset. First, a deep-cascaded neural network algorithm was used to realize face detection and eye positioning. Second, according to the eye selection mechanism, the pictures of the eyes to be tested were cropped and passed into the deep-fusion neural network to determine the eye state. Finally, the PERCLOS indicator was combined to detect the fatigue state of the controller. On the ZJU, CEW and ATCE datasets, the accuracy, F1 score and AUC values of different networks were compared, and, on the ZJU and CEW datasets, the recognition accuracy and AUC values among different methods were evaluated based on a comparative experiment. The experimental results show that the deep-fusion neural network model demonstrated better performance than the other assessed network models. When applied to the controller eye dataset, the recognition accuracy was 98.44%, and the recognition accuracy for the test video was 97.30%.

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Section: Methodsmentioning

confidence: 99%