A Comparative Analysis of Machine Learning Algorithms in Detecting Deceptive Behaviour in Humans using Thermal Images

Ravindran, Adithia; Krishna, G Gokul; Sagara,; Sarath, S

doi:10.1109/iccsp.2019.8697911

Cited by 14 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thermal camera specifications usually include high thermal resolution, the ability to record images at high speed, and the ability to measure temperature over long distances. Some brands of thermal cameras commonly used are FLIR [1,4,17,18,19] and Seek Thermal [20].…”

Section: Image Acquisitionmentioning

confidence: 99%

Implementation of Thermal Camera for Human Stress Detection: A Review

Hendryani,

Vita Nurdinawati,

Andy Sambiono

2023

IJECBE

View full text Add to dashboard Cite

Stress has become a major problem that people face today. The high level of competition and environmental demands make people more susceptible to stress. Stress can interfere with a person's ability to work effectively. If left unchecked for a long time, stress can cause various dangerous diseases such as hypertension, heart problems, and others that can lead to death. Research has been conducted for a long time to detect stress. Various technologies have been used to detect and anticipate stress that occurs in humans. One promising technology for detecting stress is the use of thermal cameras. Thermal cameras have several advantages: being non-contact and non-invasive, quick, easy to use, and cost-effective. In general, the architecture of the stress detection system using a thermal camera consists of several stages, including image acquisition, pre-processing, ROI tracking and selection, feature extraction, and statistical analysis or classification. This paper aims to review the use of thermal cameras in detecting stress in humans. This paper also seeks to answer the research question of what analysis can be done to improve stress detection accuracy using thermal camera images. Research shows that ROI selection must be carefully considered to obtain good accuracy. Combining thermal images with other data can improve accuracy in stress detection. Machine learning in classification provides many benefits in recognizing patterns but is highly influenced by the number of datasets used.

show abstract

Section: Image Acquisitionmentioning

confidence: 99%

Implementation of Thermal Camera for Human Stress Detection: A Review

Hendryani,

Vita Nurdinawati,

Andy Sambiono

2023

IJECBE

View full text Add to dashboard Cite

show abstract

“…A representative sample from the FER-2013 dataset. Table 4.1 shows the information and specification of the Dataset FER-2013 [4]. Static FER methods rely on static facial traits, but facial expressions are inherently dynamic.…”

Section: Probable Implimentationmentioning

confidence: 99%

A Survey of TensorFlow and CNN in Machine Learning for Recognizing Human Facial Expression

Nasre¹

2022

IJSREM

View full text Add to dashboard Cite

Emotion provides important details about human communication. During a conversation, it is customary to utilize facial expressions to express emotions. Furthermore, some interpersonal communication can be accomplished only through facial expressions. Some facial expressions are universal in that they convey the same feeling regardless of culture. If a machine could correctly perceive its user's facial expression, it might be able to assist them more quickly. This study creates a modular multi-channel deep convolutional neural network to improve face emotion recognition. A global average layer is used in the network output to avoid overfitting. The model's generalization ability can be improved by enhancing the dataset before training. Network offers a few advantages over other recognition algorithms. Finally, the trained recognition model is used to build a real-time face emotion recognition system. The results of the experiments will reveal that the system is capable of recognizing facial expressions in videos and photos. Keywords - convolutional neural network; network performance; expression recognition; machine learning; CNN; fer2013; TensorFlow;

show abstract