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In this paper, an efficient concealed weapon detection algorithm is proposed which uses the characteristics of human visual system in frame let domain. The main idea is to decompose the visual and IR/MMW images to be fused into low and high frequency bands using frame let transform. The fusion is performed by two different strategies while exploiting the characteristics of low and high frequency bands. The first strategy is adaptive weighted average based on local energy and is applied to fuse the low-frequency bands. In order to fuse high frequency bands, a new strategy is developed based on texture while exploiting the human visual system characteristics, which can preserve more details from source images and further improve the quality of detected weapons in the fused image. Experimental results demonstrate the efficiency and robustness of the proposed concealed weapon detection algorithm in visual inspection and objective evaluation criteria.