In driver assistance or self-driving systems, millimeter-wave radar is an indispensable sensing tool because of its applicability to all weather conditions or non-line-of-sight (NLOS) sensing. This study focuses on a human recognition issue in the NLOS scenario by applying the support vector machine (SVM)-based machine learning approach to a diffraction signal. We show that there is a significant difference in diffraction signals between man-made objects (e.g., metallic cylinder and human body) even without motion. Hence, by exploiting such difference, SVM achieves a high recognition rate, even in deeply NLOS situations. The experimental investigation, using a 24 GHz millimeter-wave radar in an anechoic chamber demonstrates that a diffraction signal based recognition accurately classifies the real human and human mimicking manmade object, even in the NLOS scenario shielded by the parking vehicle.