Gesture Feature Extraction and Recognition Based on Image Processing

Wang, Yingnan; Yang, Yueming; Zhang, Peiye

doi:10.18280/ts.370521

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…Maximum inter-class variance method [22] is the most popular way to determine the threshold. Based on the maximum variance between foreground and background, this method can find the best threshold to divide an image into foreground and background.…”

Section: Figure 1 Flow Of Video Sampling In Preprocessingmentioning

confidence: 99%

Recognition of Student Classroom Behaviors Based on Moving Target Detection

Wu¹,

Wang²,

Huang³

et al. 2021

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Classroom teaching, as the basic form of teaching, provides students with an important channel to acquire information and skills. The academic performance of students can be evaluated and predicted objectively based on the data on their classroom behaviors. Considering the complexity of classroom environment, this paper firstly envisages a moving target detection algorithm for student behavior recognition in class. Based on region of interest (ROI) and face tracking, the authors proposed two algorithms to recognize the standing behavior of students in class. Moreover, a recognition algorithm was developed for hand raising in class based on skin color detection. Through experiments, the proposed algorithms were proved as effective in recognition of student classroom behaviors.

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Section: Figure 1 Flow Of Video Sampling In Preprocessingmentioning

confidence: 99%

Recognition of Student Classroom Behaviors Based on Moving Target Detection

Wu¹,

Wang²,

Huang³

et al. 2021

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“…Figure 4 depicts the core architecture of Inceptionv3. The InceptionV3 model is used to extract features [11] and classify image datasets. More improvements are included in this network design, such as the RMSProp optimizer, factorized 7*7 convolutions, batch normalisation, and label smoothing [12][13][14].…”

Section: Classification Of Images Using Fine-tuned Inceptionv3 Architecturementioning

confidence: 99%

Detection of Hidden Facial Surface Masking in Stored and Real Time Captured Images: A Deep Learning Perspective in Covid Time

Jayaswal¹,

Dixit²

2021

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A novel coronavirus has spread over the world and has become an outbreak. This, according to a WHO report, is an infectious disease that aims to spread. As a consequence, taking precautions is the only method to avoid catching this virus. The most important preventive measure against COVID-19 is to wear a mask. In this paper, a framework is designed for face mask detection using a deep learning approach. This paper aims to predict a person having a mask or unmask and also presents a proposed dataset named RTFMD (Real-Time Face Mask Dataset) to accomplish this objective. We have also taken the RFMD dataset from the internet to analyze the performance of system. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is applied at the time of pre-processing to enhance the visual quality of images. Subsequently, Inceptionv3 model used to train the face mask images and SSD face detector model has been used for face detection. Therefore, this paper proposed a model CLAHE-SSD_IV3 to classify the mask or without mask images. The system is also tested at VGG16, VGG19, Xception, MobilenetV2 models at different hyperparameters values and analyze them. Furthermore, compared the result of the proposed dataset RTFMD with the RFMD dataset. Additionally, proposed approach is compared with the existing approach on Face Mask dataset and RTFMD dataset. The outcomes have obtained 98% test accuracy on this proposed dataset RTFMD while 97% accuracy on the RFMD dataset in real-time.

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“…Advances in information and communication technologies over the past years have expanded the scope of application of human skin detection: face detection [1][2][3], visual tracking for surveillance [4], gesture recognition [5], image retrieval and filtering image contents on the web [6], face recognition system [7,8], to mention just a few examples.…”

Section: Introductionmentioning

confidence: 99%

Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation

Soltani¹,

Benabdelkader²

2021

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The human color skin image database called SFA, specifically designed to assist research in the area of face recognition, constitutes a very important means particularly for the challenging task of skin detection. It has showed high performances comparing to other existing databases. SFA database provides multiple skin and non-skin samples, which in various combinations with each other allow creating new samples that could be useful and more effective. This particular aspect will be investigated, in the present paper, by creating four new representative skin samples according to the four rules of minimum, maximum, mean and median. The obtained samples will be exploited for the purpose of skin segmentation on the basis of the well-known Euclidean and Manhattan distance metrics. Thereafter, performances of the new representative skin samples versus performances of those skin samples, originally provided by SFA, will be illustrated. Simulation results in both SFA and UTD (University of Texas at Dallas) color face databases indicate that detection rates higher than 92% can be achieved with either measure.

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Gesture Feature Extraction and Recognition Based on Image Processing

Cited by 13 publications

References 24 publications

Recognition of Student Classroom Behaviors Based on Moving Target Detection

Recognition of Student Classroom Behaviors Based on Moving Target Detection

Detection of Hidden Facial Surface Masking in Stored and Real Time Captured Images: A Deep Learning Perspective in Covid Time

Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation

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