The Automatic Detection of Underage Troopers from Live- Videos Based on Deep Learning

Waisi, Najwan

doi:10.15199/48.2021.09.18

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Convolutional neural networks (CNNs) are commonly used for object detection and recognition tasks because of their precise yet simple architecture [23]- [25]. In this work, an algorithm from scratch has been written based on CNN for face mask and emotion detection.…”

Section: Face Mask and Emotion Detectormentioning

confidence: 99%

Masked face with facial expression recognition based on deep learning

Abdullah

Alkababji

2022

IJEECS

View full text Add to dashboard Cite

Wearing masks contributed to slowing the spread of the coronavirus disease (COVID-19) as the World Health Organization (WHO) recommended wearing face masks especially with the spreading of virus variants like omicron. Although people accept the idea of wearing these masks, it is still unknown the effect of covering parts of the face on social interaction among people in general and children in particular. Moreover, Social isolation affects emotional moods, which causes stress, sadness, and depression. In the current study, we have been exploring the emotional inferences on faces with and without a mask. The system can pick up the universal emotions: fear, disgust, anger, surprise, contempt, sadness, and happiness. The researchers in deep learning are concerned with global pandemic COVID-19 to enhance public health service. The proposed model is developed with a machine learning algorithm through the Haar feature-based cascade classifiers. The built model can detect people's emotions with mask and without a mask with high accuracy.

show abstract

Section: Face Mask and Emotion Detectormentioning

confidence: 99%

Masked face with facial expression recognition based on deep learning

Abdullah

Alkababji

2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…Deep learning is a form of machine learning that makes the computer learn through experiments to collect knowledge and understand the world [12], [13]. One of the deep learning classes is a convolutional neural network (CNN), commonly utilized for image analysis [14]- [16]. It mimics the neurons connectivity method in human brains [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Driver drowsiness monitoring system based on facial Landmark detection with convolutional neural network for prediction

2022

View full text Add to dashboard Cite

Several factors often contribute to car accidents, most of them caused by human error, and the most notable are drowsiness, fatigue, distracted driving, and alcohol. Although self-driving cars are the best solution to save human lives and avoid car accidents, they are expensive. The roads in many countries are not prepared for the movement of this type of car. Scare new technologies included in modern cars, such as backup cameras and sensors, contributed to keeping drivers safer in this paper. A driver monitoring system is based on determining the driver’s face’s main points, which provide the required vital information for face analysis. The EfficientNet convolutional neural network (ConvNet) model is used for facial landmarks prediction, which is employed to detect face drowsiness and fatigue in real-time. The system is trained to detect multiple traits, including facial expressions, yawning and head poses. The results show that employing facial landmarks will assist in efficiently producing eyes and mouth features, which can assist in appropriately creating models to analyze drowsiness. Due to this, the proposed safety features are applicable and available in future vehicles.

show abstract

The Automatic Detection of Underage Troopers from Live- Videos Based on Deep Learning

Cited by 2 publications

References 0 publications

Masked face with facial expression recognition based on deep learning

Masked face with facial expression recognition based on deep learning

Driver drowsiness monitoring system based on facial Landmark detection with convolutional neural network for prediction

Contact Info

Product

Resources

About