Real Time Object Detection using YOLO Algorithm

Haritha, IVSL; Harshini, M.; Patil, Shruti; Philip, Jeethu

doi:10.1109/iceca55336.2022.10009184

Cited by 4 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When compared to human micro-expression detection performance, this scheme is initial to recognize unexpected FME. It shows great promise [15]. As a result of Parkinson's disease, we anticipated that deliberate facial expressions may be delayed (bradykinetic) and entail less movement, just as other purposeful motions are impacted.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model

Durga,

Rajesh,

Jagannadham

et al. 2023

View full text Add to dashboard Cite

The scientific community and media have increasingly recognized the significance of microexpressions as indicators for detecting deception, as they reveal genuine emotions that individuals attempt to conceal. To capitalize on these subtle cues of deceit, researchers have developed applications capable of automatically detecting and recognizing microexpressions, which are typically imperceptible to the human eye. Facial expressions serve as fundamental ground truth determinants in multimedia applications. Earlier models, such as GA, RFO, X-Boosting, and Gradient Boosting, demonstrate greater efficiency in terms of time and accuracy. However, not all applications are capable of detecting micro facial expressions. In this study, a deep learning-based Tiefes FCNN model is designed specifically for micro facial expression recognition. Implemented using Python software, the proposed model consists of two stages: first, pre-processing is performed using image segmentation, followed by the application of a deep learning model employing Tiefes FCNN technology in the second stage. The experimental results exhibit significant performance improvements, including an accuracy of 99.02%, precision of 98.82%, F1-score of 97.8%, PSNR of 56.31, and CC of 96.31.

show abstract

Section: Literature Surveymentioning

confidence: 99%