Driver drowsiness detection system using hybrid approach of convolutional neural network and bidirectional long short term memory (CNN_BILSTM)

Rajamohana, S. P.; Radhika, E. G.; Priya, S.; Sangeetha, S.

doi:10.1016/j.matpr.2020.11.898

Cited by 28 publications

(23 citation statements)

References 14 publications

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“…On the other hand, the use of deep learning techniques, especially Recurrent Neural Networks (RNN) such as Long Short Term Memory (LSTM) [62], will be explored. This type of network allows working naturally with time series and modelling the temporal behaviour in the internal structure of the network [63]- [65].…”

Section: Discussionmentioning

confidence: 99%

Behavioural Curves Analysis Using Near-Infrared-Iris Image Sequences

Causa¹,

Tapia²,

Lopez-Droguett³

et al. 2022

Preprint

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This paper proposes a new method to estimate behavioural curves from a stream of Near-Infra-Red (NIR) iris video frames. This method can be used in a Fitness For Duty system (FFD). The research focuses on determining the effect of external factors such as alcohol, drugs, and sleepiness on the Central Nervous System (CNS). The aim is to analyse how this behaviour is represented on iris and pupil movements and if it is possible to capture these changes with a standard NIR camera. The behaviour analysis showed essential differences in pupil and iris behaviour to classify the workers in "Fit" or "Unfit" conditions. The best results can distinguish subjects robustly under alcohol, drug consumption, and sleep conditions. The Multi-Layer-Perceptron and Gradient Boosted Machine reached the best results in all groups with an overall accuracy for Fit and Unfit classes of 74.0% and 75.5%, respectively. These results open a new application for iris capture devices.

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Section: Discussionmentioning

confidence: 99%

Behavioural Curves Analysis Using Near-Infrared-Iris Image Sequences

Causa¹,

Tapia²,

Lopez-Droguett³

et al. 2022

Preprint

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“…In 2021, Quddu et al [3] have proposed a novel eyetracking system using the RNN and LSTM. The authors have utilized the RNN to detect the drowsiness.…”

Section: Related Workmentioning

confidence: 99%

“…The assessment has been carried out in terms of MAE, MAPE, MSE, MSLE, RMSE and Optimization error as well. the proposed work (CNN+SOEE) has been compared over the existing models like the CNN+Bi-LSTM [2], C-LSTM [3], Bi-LSTM, RNN, MFO+CNN, GOA+CNN, SLnO+CNN and SOA+CNN, respectively. The correctly detected eye-blinks are denoted as True Positives (TP), false detections are denoted as False Positives (FP), and missed eye-blinks are denoted as False Negatives (FN) [24].…”

Section: A Experimental Setupmentioning

confidence: 99%

“…In this research work, the MAE of the proposed work has been computed, and its results acquire are compared over the existing models like the CNN+Bi-LSTM [27], C-LSTM [3],Bi-LSTM, RNN, MFO+CNN, GOA+CNN, SLnO+CNN and SOA+CNN, respectively. This evaluation has been carried out by varying the learning percentage from 60, 70, 80 and 90, respectively.…”

Section: Mae Analysismentioning

confidence: 99%

“…We rely upon the smartphones for storing our confidential information. In the smartphones, the spoofing attacks can be avoided by utilizing the eye blink detection techniques within the face recognition systems [1][2][3][4] Moreover, the eye-blink detection systems in the smartphones provides information regarding the eyeblinking habits of the users, and provides advices [5][6][7][8]. In fact, eye blinking is said to be the partly subconscious quick closing as well as reopening of the eyelids.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Learning Approach for Efficient Eye-blink Detection with Hybrid Optimization Concept

Gawande,

Badotra

2022

IJACSA

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In this research work, a novel eye-blink detection model is developed. The proposed eye blink detection model is modeled by following seven major phases: (a) video-to-frame conversion, (b) Pre-processing, (c) face detection, (d) eye region localization, (e) eye landmark detection and eye status detection, (f) eye blink detection and (f) Eye blink Classification. Initially, from the collected raw video sequence (input), each individual frames are extracted in the video -to-frame conversion phase. Then, each of the frames is subjected to pre-processing phase, where the quality of the image in the frames is improved using proposed Kernel median filtering (KMF) approach. In the face detection phase, the Viola-Jones Model has been utilized. Then, from the detected faces, the eye region is localization within the proposed eye region localization phase. The proposed Eye region localization phase encapsulates two major phases: Feature extraction and landmark detection. The features like improved active shape models (I-ASMs), Local Binary pattern are extracted from the detected facial images. Then, the eye region is localization by using a new optimized Convolution neural network framework. This optimized CNN framework is trained with the extracted features (I-ASM and LBP). Moreover, to enhance the classification accuracy of eye localization, the weight of CNN is fine-tuned using a new Seagull Optimization with Enhanced Exploration (SOEE), which is the improved version of standard Seagull Optimization Algorithm (SOA). The outcome from optimized CNN framework is providing the exact location of the eye region. Once the eye region is detected, it is essential to detect the status of the eye (whether open or close). The status of the eye is detected by computing the eye aspect ratio (EAR). Then, the identified eye blinks are classified based on the computed correlation coefficient as long and short blinks. Finally, a comparative evaluation has been accomplished to validate the projected model.

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