Entropy-Based Facial Movements Recognition Using CPVM

Kehri, Vikram; Puri, Digambar; Awale, R. N.

doi:10.1007/978-981-15-4029-5_2

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Support Vector Machine (SVM) is a linear classifier. Our system will use this classifier in such a way that it can take all the measurements of unrecognized faces and it will compare them with the measurements of all the images present in our database [16]- [18]. After comparing, it will give us the name of that person whose measurements were closest match.…”

Section: Comparison Using Svmmentioning

confidence: 99%

Face recognition Attendance system using HOG and CNN algorithm

Kapse¹,

Kamble²,

Lohar³

et al. 2022

ITM Web Conf.

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Recognition of faces is one of the most useful applications and has a critical role in the technological field. Recognizing the face is a lively concern for authentication, specifically in the context of taking attendance. Attendance system using face recognition is a process of recognizing the profile of the person by using facial features supported by various computing technology and monitoring. The evolution of this process is focused on achieving the digitizing of the orthodox system of taking manual attendance. Current approaches for taking attendance are monotonous and inefficient. Manual records of attendance can be easily manipulated. The orthodox process of checking attendance such as current fingerprint or card scanning systems are susceptible to proxies. To tackle these issues, this paper has been proposed. The proposed system makes the utilization of various algorithms such as histogram of oriented Gradient (HOG), convolutional neural network (CNN) and support vector machine (SVM). After the face is recognized, the reports of attendance are going to be created, maintained and stored in excel format. The system is examined in various situations like illumination, head movements, and the variation of distance between the face and cameras. The proposed system was found to be efficient and reliable for marking attendance during a classroom with negligible time consumption and no manual work. This system is inexpensive as less installation is required.

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Section: Comparison Using Svmmentioning

confidence: 99%

Face recognition Attendance system using HOG and CNN algorithm

Kapse¹,

Kamble²,

Lohar³

et al. 2022

ITM Web Conf.

Self Cite

View full text Add to dashboard Cite

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“…We have used the "EEG Eye State Dataset" from UCI Machine Learning Repository as the common dataset for all computations [5]. This data is acquired from an EEG neuroheadset (Emotiv EEG Neuroheadset) for a duration of 117 seconds and collects 14304 samples.…”

Section: Datasets and Inputsmentioning

confidence: 99%

Drowsiness Detection using EEG signals and Machine Learning Algorithms

Joshi,

Kamble,

Parate

et al. 2022

ITM Web Conf.

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Drowsiness is described as a state of reduced consciousness and vigilance accompanied by a desire or want to sleep. Driver tiredness is frequently detected using wearable sensors that track vehicle movement and camera-based systems that track driver behavior. Many alternative EEG-based drowsiness detection systems are developed due to the potential of electroencephalogram (EEG) signals to observe human mood and the ease with which they may be obtained. This paper applies Deep learning architecture like Convolutional Neural networks (CNN) and algorithms for the classification of EEG data for Drowsiness Detection. The key measures of video-based approaches include the detection of physical features; nevertheless, problems such as brightness limitations and practical challenges such as driver attention limits its usefulness. The main measure of video-based methods is the degree of closure of the eyelids; however, its success is limited by constraints like as brightness restrictions and practical challenges such as driver distraction. We have extracted statistical features and trained using various classifiers like Logistic Regression, Naïve Bayes, SVM, and K Nearest Neighbours and compared the accuracy using a deep learning CNN model. Results demonstrate that CNN achieved an accuracy of 94.75% by delegating feature extraction on itself. Upon comparing existing state–of–the–art drowsiness detection systems, the testing results reveal a higher detection capability. The results show that the the suggested method can be used to develop a reliable EEG-based driving drowsiness detection system.

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