# An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition
## 4 Experimental Results
### Experiments on LFW  
Labeled Faces in the Wild (LFW) [55] is composed of images collected over the web. For this reason, the dataset is quite rich in terms of variations in photo quality, pose, illumination, resolution, age, race, accessories, make-up, occlusion, background, and facial expression. There are 13,233 face images detected by the Viola-Jones face detector [67], and 5749 different people in this dataset. There is a different number of images for each person, and the number of people with two or more images is 1680. Within LFW, there are two separate sets called View1 and View2. View1 was used for model selection and algorithm development, and View2 was used for calculating the success of the used methods. View2 has 10 subsets, and the images in the subsets do not overlap with each other. In each subset, there are 300 matched and 300 unmatched pairs. While performance was being calculated, 10-fold cross-validation was performed using 10 subsets. The performance for each subset was calculated separately and the remaining 9 subsets were used for training operations. In studies conducted in the literature, the results obtained are generally given in six different categories. Unsupervised methods are compared in one of them, and supervised methods are compared in the other. In this study, since an unsupervised method is used, the results obtained are given together with the best known results in the unsupervised category. In many studies in the literature, besides the original LFW, frontalized versions of this dataset are also used. In this study, we use the images which are frontalized with the \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Fb** & **Fc** & **Dup1** & **Dup2** & **Avg** \\ \hline LMEGW//LN + LGXP [64] & 99.9 & 100 & 94.7 & 91.9 & 97.5 \\ s-POEM + POD + WPCA [65] & 99.7 & 100 & 94.9 & 94.0 & 97.7 \\ GOM [66] & 99.9 & 100 & 95.7 & 93.1 & 97.9 \\ LMEGW//LN + LGBP [64] & 99.9 & 100 & 95.6 & 93.6 & 98.0 \\ Basaran et al. [42] & 99.8 & 100 & 96.0 & 94.9 & 98.2 \\ MDML-DCPs + WPCA [2] & 99.8 & 100 & 96.1 & 95.7 & 98.3 \\ Basaran et al. [53] & 99.8 & 100 & 97.5 & 96.6 & 98.8 \\ LPOG + WPCA [46] & 99.8 & 100 & 97.4 & 97.0 & 98.8 \\ \hline MS-LZM & 99.7 & 100 & 98.1 & 97.9 & 99.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Identification rates on FERET with state-of-the-art results. MS-LZM: multi-scale local Zernike moments. method proposed by Zhu et al. [49]. Sample images (original and frontalized) of this dataset are given in Figure 9. To measure the verification performance on LFW in the unsupervised category, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used as specified in its standard protocol [55]. The AUC value for each subset was calculated, and the average of these AUC values is reported. Table 2 shows the results obtained using MS-LZM for LFW together with the best known results in the literature. While these results were calculated, face images were used in five different scales, and the sizes used for images were the same as those used in FERET experiments. For first and second LZM transformations applied on multi-scale images, the moment degrees were four and the filter sizes were seven. When the experiments were performed on this dataset, WPCA matrices were learned using 1200 images randomly selected from nine subsets other than the subset used for the performance calculation. As in Table 1, in the methods compared in Table 2, the features for face images were computed using local operators. With MS-LZM, we obtained better results than the others except for MRF-Fusion-CSKDA [73]. In MRF-Fusion-CSKDA (which is proposed only for the verification problem), features were obtained with three different local methods (Multiscale Local Binary Patterns (MLBP) [74], Multiscale Local Phase Quantization (MLPQ) [75], and Multiresolution of Binarised Statistical Image Features (MBSIF) [76]), and these features were fused with a nonlinear binary classifier