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This paper presents a new keypoint-based approach for Near-Duplicate (ND) keyframes identification using global and local features. In the proposed method, the matched keypoints are filtered for affine transform estimation based on an affine invariant feature which is using the lesser matching pairs and voting bins. To further confirm the matching, the Local Binary Pattern (LBP) and color features of areas formed by matched keypoints in two images are compared. This method combines the advantage of global and local matching, it is able to identify two images related with affine transform. The proposed algorithm has been tested in Columbia dataset and conducted the quantitative comparison with RANSAC algorithm and SR-PE algorithm. The experiment results show that the proposed method can achieve good results.