| Reference | Database Size | Data Representation | Algorithm | Reported Performance |
| Cartoux et al. [10], 1989 | 18 | Profiles, surface | Curvature based Nearest Neighbor | 100% |
| Lee et al. [11], 1990 | 6 | EGI | Correlation | N/A |
| Gordon, [12], 1992 | 26 | curvatures | Euclidean nearest neighbor | 100% |
| Nagamine et al. [6], 1992 | 160 | Multiple profiles | Euclidean nearest neighbor | 100% |
| Tanaka et al. [13], 1998 | 37 | curvatures based EGI | Fisher’s spherical Correlation | 100% |
| Zhao and Chellappa [14], 2000 | N/A | 3D model + Texture | Produce a prototype images | 81% |
| Beumier et al. [15], 2001 | 120 | 2D and 3D vertical profiles | Minimum distance, fusion | 1.4% EER |
| Wang et al. [16], 2002 | 300 | Feature vector point signature Gabor features | PCA + SVM | >90% |
| Chang et al. [17], 2003 | 278 | Texture + Range image | PCA | 99% 3D + 2D, 93% 3D only |
| Hesher et al. [8], 2003 | 222 | Range image | ICA or PCA, nearest neighbor | 97% |
| Moreno et al. [18], 2003 | 420 | Curvature, line, region features | Euclidean nearest neighbor | 78% |
| Pan et al [19], 2003 | 360 | Point set, range image | Hausdorff and PCA | 3% - 5% EER |
| Chang et al. [20], 2003 | 278 | Texture+ Range image | PCA | 99% 3D + 2D, 93% 3D only |
| Tsalakanidou, [21] 2003 | 80 | Range image | PCA | 99% |
| Xu et al. [4], 2004 | 30 &120 | Point set + feature vector | Minimum distance | 96% on 30, 72% on 120 |
| Lu et al. [22], 2005 | 196 | Surface mesh | ICP, TPS | 89% |
| Lee et al.[23], 2005 | 70 | depth map | Feature extraction, nearest neighbor | 94% |
| Bronstein et al.[24], 2005 | 220 | Point set | Canonical forms | 100% |
| Lee et al. [25], 2005 | 200 | Feature vector | SVM | 96% |
| Pan et al. [26], 2005 | 720 | Range image | PCA | 95% |
| Zhang et al. [27], 2006 | 32 | Range images | mean curvature | 96.9% |
| Qatawnah et al. [28], 2008 | 56 | Range images | Feature extraction, CCNN, SVM and KNN | 100% |
| Elyan and Ugail [19], 2009 | 144 | triangle mesh | Coon’s surface patch | 86.9% |