Deep Learning Framework to Detect Face Masks from Video Footage

Joshi, Aniruddha Srinivas; Joshi, Saurabh; Kanahasabai, Goutham; Kapil, Rudraksh; Gupta, Savyasachi

doi:10.1109/cicn49253.2020.9242625

Cited by 62 publications

(21 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It proves that the masked or non-masked face classification using the DenseNet201 model is better than using the MobileNetV2 model. These results prove that face masks' detection using the DenseNet201 model is better than previous research [7]. The number of parameters for the DenseNet201 model is 20 M while the MobileNetV2 model is only 3.5 M. Fig.…”

Section: B Testing Results Analysissupporting

confidence: 57%

See 1 more Smart Citation

A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face

Adhinata¹,

Rakhmadani²,

Wibowo³

et al. 2021

Jurnal Informatika

View full text Add to dashboard Cite

The use of masks on the face in public places is an obligation for everyone because of the Covid-19 pandemic, which claims victims. Indonesia made 3M policies, one of which is to use masks to prevent coronavirus transmission. Currently, several researchers have developed a masked or non-masked face detection system. One of them is using deep learning techniques to classify a masked or non-masked face. Previous research used the MobileNetV2 transfer learning model, which resulted in an F-Measure value below 0.9. Of course, this result made the detection system not accurate enough. In this research, we propose a model with more parameters, namely the DenseNet201 model. The number of parameters of the DenseNet201 model is five times more than that of the MobileNetV2 model. The results obtained from several up to 30 epochs show that the DenseNet201 model produces 99% accuracy when training data. Then, we tested the matching feature on video data, the DenseNet201 model produced an F-Measure value of 0.98, while the MobileNetV2 model only produced an F-measure value of 0.67. These results prove the masked or non-masked face detection system is more accurate using the DenseNet201 model.

show abstract

Section: B Testing Results Analysissupporting

confidence: 57%

“…One of the deep learning techniques is using transfer learning, where the model has been trained using the ImageNet dataset [10]. Researchers Joshi et al [7] using transfer learning MobileNetV2 resulted in an F-measure value below 0.9. The results of this F-measure indicate that the system being built is not accurate enough.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face

Adhinata¹,

Rakhmadani²,

Wibowo³

et al. 2021

Jurnal Informatika

View full text Add to dashboard Cite

show abstract

“…Face mask detection algorithms have become more topical recently, since masks can help control the spread of COVID-19 during the pandemic. The algorithmic task focuses only on detecting physical masks, as shown in [18], [20], [21], [23], [34], [35]. Among these, YOLO based models are the most popular detectors.…”

Section: B Face Mask Detectionmentioning

confidence: 99%

“…Several studies have explored the detection of face masks. One approach is a two-step method which firstly detects faces using face detectors and then separately classifies whether a face mask is worn based on face mask classifiers [18], [19]. Although two-step methods may be sufficient in some scenarios, the operation of passing the results from the first step to the second step can degrade the speed significantly.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Based Light-Weight Face Mask Detector With Residual Context Attention and Gaussian Heatmap to Fight Against COVID-19

2021

View full text Add to dashboard Cite

Coronavirus disease 2019 has seriously affected the world. One major protective measure for individuals is to wear masks in public areas. Several regions applied a compulsory mask-wearing rule in public areas to prevent transmission of the virus. Few research studies have examined automatic face mask detection based on image analysis. In this paper, we propose a deep learning based single-shot light-weight face mask detector to meet the low computational requirements for embedded systems, as well as achieve high performance. To cope with the low feature extraction capability caused by the light-weight model, we propose two novel methods to enhance the model's feature extraction process. First, to extract rich context information and focus on crucial face mask related regions, we propose a novel residual context attention module. Second, to learn more discriminating features for faces with and without masks, we introduce a novel auxiliary task using synthesized Gaussian heatmap regression. Ablation studies show that these methods can considerably boost the feature extraction ability and thus increase the final detection performance. Comparison with other models shows that the proposed model achieves state-of-the-art results on two public datasets, the AIZOO and Moxa3K face mask datasets. In particular, compared with another light-weight you only look once version 3 tiny model, the mean average precision of our model is 1.7% higher on the AIZOO dataset, and 10.47% higher on the Moxa3K dataset. Therefore, the proposed model has a high potential to contribute to public health care and fight against the coronavirus disease 2019 pandemic.

show abstract

“…Typical surveillance cameras applications include public safety, protection of facilities against theft or vandalism, remote video monitoring, traffic surveillance, weather monitoring, or more special cases, such as animal monitoring or data collection, for statistical or marketing purposes. Today, due to the pandemic situation, face recognition with and without a protective mask is also becoming a point of interest for researchers in cooperation with technology companies [ 1 , 2 , 3 ]. It is important to realize that each such employed sensor produces a tremendous amount of data to be subsequently transmitted over the network or further processed, which calls for effective video compression.…”

Section: Introductionmentioning

confidence: 99%

Impact of Scene Content on High Resolution Video Quality

Uhrina

Holesova

Bienik

et al. 2021

Sensors

View full text Add to dashboard Cite

This paper deals with the impact of content on the perceived video quality evaluated using the subjective Absolute Category Rating (ACR) method. The assessment was conducted on eight types of video sequences with diverse content obtained from the SJTU dataset. The sequences were encoded at 5 different constant bitrates in two widely video compression standards H.264/AVC and H.265/HEVC at Full HD and Ultra HD resolutions, which means 160 annotated video sequences were created. The length of Group of Pictures (GOP) was set to half the framerate value, as is typical for video intended for transmission over a noisy communication channel. The evaluation was performed in two laboratories: one situated at the University of Zilina, and the second at the VSB—Technical University in Ostrava. The results acquired in both laboratories reached/showed a high correlation. Notwithstanding the fact that the sequences with low Spatial Information (SI) and Temporal Information (TI) values reached better Mean Opinion Score (MOS) score than the sequences with higher SI and TI values, these two parameters are not sufficient for scene description, and this domain should be the subject of further research. The evaluation results led us to the conclusion that it is unnecessary to use the H.265/HEVC codec for compression of Full HD sequences and the compression efficiency of the H.265 codec by the Ultra HD resolution reaches the compression efficiency of both codecs by the Full HD resolution. This paper also includes the recommendations for minimum bitrate thresholds at which the video sequences at both resolutions retain good and fair subjectively perceived quality.

show abstract

Deep Learning Framework to Detect Face Masks from Video Footage

Cited by 62 publications

References 11 publications

A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face

A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face

A Deep Learning Based Light-Weight Face Mask Detector With Residual Context Attention and Gaussian Heatmap to Fight Against COVID-19

Impact of Scene Content on High Resolution Video Quality

Contact Info

Product

Resources

About