Abstract: Since it treats the parts equally, while the deformable parts model (DPM) cannot highlight distinctive parts that are helpful to distinguishing subtle categories. To cope with the problem mentioned above, a weighted coefficient deformable parts model (WCDPM) was proposed to highlight distinctive parts and decrease the influence of non-distinctive parts, which leaded to improving performance in terms of fine-grained recognition accuracy. The detailed processes of model training and coefficient learning were also presented. Experimental results of Airplan OID and Oxford-ⅢT Pet data sets demonstrate the effectiveness of the proposed method.