Abstract: Target areas in infrared thermal images in outdoor environments are affected by background overheating and the surrounding environment, causing fuzzy target boundaries and large noise. An inner-canthus location algorithm for infrared thermal images in outdoor environments is proposed to solve this problem. First, the algorithm corrects the facial tilt of collected images. Then, Gentle-Adaboost and Haar features are combined to perform approximate localization of human faces and eyes, and geometric correction is applied to accurately locate the eye region. Finally, based on the characteristics of the inner-canthus region, an inner-canthus location is proposed by combining region refinement and region growth segmentation. Experiments are conducted on three different infrared thermal image datasets and outdoor datasets independently collected at different temperature ranges in different seasons. The results show that the proposed method can effectively locate the inner canthus in the outdoor environment, and the accuracy for human eyes and inner-canthus can reach 98.1% and 97.7%, respectively.