In the biometric verification system of a smart gun, the rightful user of the gun is recognized based on grip-pattern recognition. It was found that the verification performance of grip-pattern recognition degrades strongly when the data for training and testing the classifier, respectively, have been recorded in different sessions. The major factors that affect the verification performance of this system are the variations of pressure distribution and hand position between the probe image and the gallery image of a subject. In this work, three methods are proposed to reduce the effect of the variations by using different sessions for training, image registration, and classifier fusion. Based on these methods, the verification results are significantly improved.