Fever is one significant sign of infection. Hence, infrared thermography systems are important for detecting infected suspects in public places. Reliable temperature measurements by thermography are influenced by several factors, including environmental conditions. This paper proposes a linear regression analysis-based facial temperature optimization method to improve the accuracy of multiple vital signs-based infection screening at various ambient temperatures. To obtain the relationship between ambient temperature and thermography measurements, 20 instances of axillary temperature, thermography measurements of facial temperature, and five different ambient temperature values at the time of measurement were used as a training set for a linear regression model. Temperatures from a total of 30 subjects were recalculated by the model. The screening system was evaluated using the temperature both before and after optimization to demonstrate the accuracy of the optimization method. A k-nearest neighbor algorithm was used to classify potentially infected patients from healthy subjects. Although the system has already been evaluated in restricted environmental conditions, this is the first time it was tested in Ulaanbaatar, Mongolia. The results show that the Pearson's correlation coefficient between optimum and axillary temperatures increased to r = 0.82. Paired t-tests revealed that the optimized temperature became statistically highly significant (p<0.001) for differentiating potentially infected patients from healthy subjects. Finally, the system achieved a sensitivity score of 91% and a negative predictive value of 92%. These values are higher than those obtained without temperature optimization. The proposed optimization method is feasible and can notably improve screening performance.