Active millimeter-wave (MMW) near-filed human imaging is a means for concealed objects detection. A method of concealed objects detection based on fast wavelet transforms (FWT) in the usage of active MMW images is presented as a result of image characteristics, which includes high resolution, characteristics varying in different parts of the human, imaging influenced among human, concealed objects and other objects, and different textures of concealed objects. Images segmentation utilizing results of edge detection based on FWT is conducted and preliminary segmentation results can be obtained. Some kinds of concealed objects according to comparing gray value of concealed objects to human average gray value can be detected in this paper. The experiments of concealed objects on images of actual acquisition are conducted with a result of accurate rate 80.92% and false alarm rate 11.78%, illustrating the effectiveness of the method proposed in this paper.