This paper examines the processing of millimeter-wave imaging data based on sparse sampling and sparse array design for the rapid imaging of human security data. First, based on the cylindrical scanning imaging model, the Barker code-based randomly sparse sampling method is employed to reduce the scanning time. Then, a three-dimensional imaging algorithm based on interferometry and compressed sensing in the frequency domain is proposed, with sparse representation of the image in the frequency domain after interferometry and Compressed Sensing measurement model, to recover the image frequency spectrum, thereby implementing human security image reconstruction via sparse sampling. Real data processing results indicated that the proposed method could obtain image resolution and performance similar to those of complete samples and that the image correlation coefficients before and after sparse sampling were better than 0.9, with 50% time/data reduction. Furthermore, based on the Barker codes and multistatic work mode, a sparse array architecture for rapid imaging was designed with a sparse rate of 94.6% and the guarantee of imaging quality. The proposed method was found to considerably increase the passage rate and reduce the amount of radiation unit and system complexity, marking its application significance and market prospect in security clearance.