Summary of Fert: Real-time Facial Expression Recognition with Short-range Fmcw Radar, by Sabri Mustafa Kahya et al.
FERT: Real-Time Facial Expression Recognition with Short-Range FMCW Radar
by Sabri Mustafa Kahya, Muhammet Sami Yavuz, Eckehard Steinbach
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study proposes a novel approach for real-time facial expression recognition using frequency-modulated continuous-wave (FMCW) radar. The system integrates four modalities: range-Doppler images, micro-range-Doppler images, range azimuth images, and range elevation images. A deep learning architecture is used to classify facial expressions into smile, anger, neutral, and no-face classes. The model achieves an average classification accuracy of 98.91% on a dataset collected using a short-range FMCW radar. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses a special kind of radar to recognize people’s facial expressions in real-time. It looks at four different types of images to figure out if someone is smiling, angry, neutral, or not showing their face. The system gets really good at recognizing these expressions and could be used in many different applications. |
Keywords
* Artificial intelligence * Classification * Deep learning
