This paper proposes an object verification method by using sparse representation (SR) which has been applied for object representation and recognition. However, SR dictionary does not show sufficient compactness. Our method comprises three major modules. First, we train the sparse matrix by using boost K-Singular Value Decomposition (boost K-SVD) to obtain a sparse vector set. Second, we combine two training sparse vector sets of the same and different objects from two views to generate a positive/negative combined sparse vector set. Finally, a Support Vector Machine (SVM) classifier is applied for verification. Our contributions are (1) obtaining a sparser vector set using K-SVD, (2) demonstrating the SR matrix with better Restricted Isometry Property (RIP), and (3) applying the SR matrix to the object verification process with high accuracy. The experimental results prove that our method has higher accuracy than the other methods.