WiFace: Facial Expression Recognition Using Wi-Fi Signals

Chen, Yanjiao; Ou, Runmin; Li, Zhiyang; Wu, Kaishun

doi:10.1109/tmc.2020.3001989

Cited by 14 publications

(2 citation statements)

References 39 publications

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“…This paper introduces a new WiFi-based method of facial expression recognition called WiFE. Our main insight is that in various expressions, facial muscular activity produces distinctive waveform patterns in channel state information (CSI) time series in Wireless Local Area Network (WLAN) signals [41]. A deep tree-based model is proposed in a cloud environment for automatic facial recognition, and the proposed deep model is less costly computationally without affecting its accuracy [42].…”

Section: Related Workmentioning

confidence: 99%

IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks

et al. 2021

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Facial emotion recognition (FER) is the procedure of identifying human emotions from facial expressions. It is often difficult to identify the stress and anxiety levels of an individual through the visuals captured from computer vision. However, the technology enhancements on the Internet of Medical Things (IoMT) have yielded impressive results from gathering various forms of emotional and physical health-related data. The novel deep learning (DL) algorithms are allowing to perform application in a resource-constrained edge environment, encouraging data from IoMT devices to be processed locally at the edge. This article presents an IoMT based facial emotion detection and recognition system that has been implemented in real-time by utilizing a small, powerful, and resource-constrained device known as Raspberry-Pi with the assistance of deep convolution neural networks. For this purpose, we have conducted one empirical study on the facial emotions of human beings along with the emotional state of human beings using physiological sensors. It then proposes a model for the detection of emotions in real-time on a resource-constrained device, i.e., Raspberry-Pi, along with a co-processor, i.e., Intel Movidius NCS2. The facial emotion detection test accuracy ranged from 56% to 73% using various models, and the accuracy has become 73% performed very well with the FER 2013 dataset in comparison to the state of art results mentioned as 64% maximum. A t-test is performed for extracting the significant difference in systolic, diastolic blood pressure, and the heart rate of an individual watching three different subjects (angry, happy, and neutral).

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Section: Related Workmentioning

confidence: 99%

IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks

et al. 2021

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“…Several parameters can be utilized for this underlying task such as the received signal strength, the TV phase of the received signal, the time-of-arrival, the time differences of arrival, and the angle-of-arrival. More papers on this topic can be found in [7,12,18,20,23,32,33].…”

Section: Introductionmentioning

confidence: 99%

A non-stationary relay-based 3D MIMO channel model with time-variant path gains for human activity recognition in indoor environments

2021

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Extensive research showed that the physiological response of human tissue to exposure to low-frequency electromagnetic fields is the induction of an electric current in the body segments. As a result, each segment of the human body behaves as a relay, which retransmits the radio-frequency (RF) signal. To investigate the impact of this phenomenon on the Doppler characteristics of the received RF signal, we introduce a new three-dimensional (3D) non-stationary channel model to describe the propagation phenomenon taking place in an indoor environment. Here, the indoor space is equipped with a multiple-input multiple-output (MIMO) system. A single person is moving in the indoor space and is modelled by a cluster of synchronized moving point scatterers, which behave as relays. We derive the time-variant (TV) channel transfer function (CTF) with TV path gains and TV path delays. The expression of the TV path gains is obtained from the instantaneous total received power at the receiver side. This TV total received power is expressed as the product of the TV power of the RF signal initially transmitted and received by a body segment and the TV received power of the redirected signal. These TV powers are determined according the free-space path-loss model. Also, a closed-form approximate solution to the spectrogram of the TVCTF is derived. Here, we analyse the effect of the motion of the person and the validity of the relay assumption on the spectrogram, the TV mean Doppler shift (MDS), and the TV Doppler shift (DS) of the TVCTF. Simulation results are presented to illustrate the proposed channel model.

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A comprehensive survey on deep facial expression recognition: challenges, applications, and future guidelines

Sajjad

Ullah

et al. 2023

Alexandria Engineering Journal

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WiFace: Facial Expression Recognition Using Wi-Fi Signals

Cited by 14 publications

References 39 publications

IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks

IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks

A non-stationary relay-based 3D MIMO channel model with time-variant path gains for human activity recognition in indoor environments

A comprehensive survey on deep facial expression recognition: challenges, applications, and future guidelines

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