Infrared Thermal Imaging Face Recognition Method Based on Temperature Block Feature
4th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2022
; : 748-752, 2022.
Article
in English
| Scopus | ID: covidwho-2052014
ABSTRACT
In order to prevent COVID-19 effectively, non-contact body temperature measurement and human identification are required in public places, but face recognition based on visible light cannot meet the requirements. Therefore, this paper proposes a thermal imaging face recognition method based on temperature block feature extraction. Histogram equalization and median filter are used to preprocess the face image, and Sobel operator is used for face detection;Six dimensional features including temperature mean, standard deviation and adjacent difference are extracted from each temperature block in the average poolinged temperature matrix, and classified by max-correlation-coefficient method. The experimental results show that the recognition rate of this method is 6.1% higher than that of PCA method with the temperature block size of boldsymbol{2times 2}. When using the same hardware to execute the program, if the two recognition rates are very close, the average test time of the proposed method is 22.2% less than the one of deep learning models such as Alexnet. Furthermore, the proposed method has strong robustness for small training sample set. For example, the recognition rate of single training sample model can reach 0.7, while in the deep learning model, except Mobilenet can reach 0.6, all of the others are less than 0.4. © 2022 IEEE.
Average pooling; Block characteristics; Correlation coefficient; Infrared thermal imaging; Small sample robustness; Deep learning; Face recognition; Feature extraction; Learning systems; Median filters; Software testing; Temperature measurement; Block characteristic; Contact bodies; Face recognition methods; Learning models; Non-contact; Small samples; Infrared imaging
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Scopus
Language:
English
Journal:
4th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2022
Year:
2022
Document Type:
Article
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