Face segmentation and detection using Voronoi diagram and 2D histogram

Jida, Safa; Aksasse, Brahim; Ouanan, Mohammed

doi:10.1109/isacv.2017.8054959

Cited by 8 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e existing face detection algorithms can be divided into two categories: one is a multilevel detection algorithm based on the proposed region. e other is the target detection algorithm based on anchor frame [19]. e representative algorithms of the former are Faster-RCNN [20] and MTCNN [21].…”

Section: Related Workmentioning

confidence: 99%

Driver Fatigue Detection Based on Facial Key Points and LSTM

Chen

Xin

Liu

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

In recent years, fatigue driving has been a serious threat to the traffic safety, which makes the research of fatigue detection a hotspot field. Research on fatigue recognition has a great significance to improve the traffic safety. However, the existing fatigue detection methods still have room for improvement in detection accuracy and efficiency. In order to detect whether the driver has fatigue driving, this paper proposes a fatigue state recognition algorithm. The method first uses MTCNN (multitask convolutional neural network) to detect human face, and then DLIB (an open-source software library) is used to locate facial key points to extract the fatigue feature vector of each frame. The fatigue feature vectors of multiple frames are spliced into a temporal feature sequence and sent to the LSTM (long short-term memory) network to obtain a final fatigue feature value. Experiments show that compared with other methods, the fatigue state recognition algorithm proposed in this paper has achieved better results in accuracy. The average accuracy of the proposed method in detecting key points of the face is as high as 93%, and the running time is less than half of the ordinary DLIB method.

show abstract

Section: Related Workmentioning

confidence: 99%

Driver Fatigue Detection Based on Facial Key Points and LSTM

Chen

Xin

Liu

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…This method effectively improves the performance of face detection, and it is still used and improved to this day. Recently, with the continuous development and application of deep learning, it provides a new method for face detection and segmentation [35]. It can be divided into two categories: One is a multi-level detection algorithm based on proposal region.…”

Section: A Face Detection Based On the Improved Yolov3-tiny Networkmentioning

confidence: 99%

A Fatigue Driving Detection Algorithm Based on Facial Multi-Feature Fusion

Gong

Ren

2020

IEEE Access

View full text Add to dashboard Cite

Researches on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms do not analyze driving state from driver characteristics. It results in some inaccuracy. The paper proposes a fatigue driving detection algorithm based on facial multifeature fusion combining driver characteristics. First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. Second, on the basis of the Dlib toolkit, we introduce the Eye Feature Vector(EFV) and Mouth Feature Vector(MFV), which are the evaluation parameters of the driver's eye state and mouth state, respectively. Then, the driver identity information library is constructed by offline training, including driver eye state classifier library, driver mouth state classifier library, and driver biometric library. Finally, we construct the driver identity verification model and the driver fatigue assessment model by online assessment. After passing the identity verification, calculate the driver's closed eyes time, blink frequency and yawn frequency to evaluate the driver's fatigue state. In simulated driving applications, our algorithm detects the fatigue state at a speed of over 20fps with an accuracy of 95.10%.

show abstract

“…In order to avoid high computational time, several authors [5,[16][17][18] proposed a new approach based on VD using the generated clusters of intensity values given in the vertices of the external boundary of Delaunay triangulation. They applied this method in order to segment face features.…”

Section: Voronoi Exploitationmentioning

confidence: 99%

Clay-Based Brick Porosity Estimation Using Image Processing Techniques

Jida

Ouallal²,

Aksasse

et al. 2019

Journal of Intelligent Systems

Self Cite

View full text Add to dashboard Cite

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.

show abstract

Face segmentation and detection using Voronoi diagram and 2D histogram

Cited by 8 publications

References 11 publications

Driver Fatigue Detection Based on Facial Key Points and LSTM

Driver Fatigue Detection Based on Facial Key Points and LSTM

A Fatigue Driving Detection Algorithm Based on Facial Multi-Feature Fusion

Clay-Based Brick Porosity Estimation Using Image Processing Techniques

Contact Info

Product

Resources

About