# Fuzzy Windows with Gaussian Processed Labels for Ordinal Image Scoring Tasks
## 5 Results and Analysis
### Scoring Facial-Age Images  
With respect to scoring facial-age images, we set the number of neurons to 10 and the length of the window to 5. The results of a comparison between our model and SOTA models on three facial-age datasets are summarized in Table 5.  
Compared with label distribution learning methods such as Deep Label Distribution Learning V2 (DLDL-V2), ref. [2] and MV Loss [3], FW-GPL leverages a fixed pattern (Gaussian processed labels) to learn features that have considered the issue of age distribution. Our FW-GPL does not need to know beforehand the age distribution of the image data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **CNN + FCL** & **CNN + FCL** & **CNN + W-GPL** \\ \hline
**[PERSON]**[47] & 68.8\% & 70.1\% & 70.3\% \\
**(BI-RADS: 0/12)** & & & \\
**[PERSON]**[48] & 60.0\% & 62.3\% & 62.4\% \\
**(BI-RADS: 2/(3-4-5))** & & & \\
**[PERSON]**[17] & 72.0\% & 74.1\% & 74.2\% \\
**(BI-RADS: 0/(2-3)/(4-5))** & & & \\
**[PERSON]**[17] & 56.34\% & 57.40\% & 58.29\% \\
**(BI-RADS: 0/1/2/3/4/5)** & \(\pm 1.4\%\) & \(\pm 1.7\%\) & \(\pm 1.9\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with existing methods on DDSM in terms of ACC.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Method** & **MORPH 2** & **FG-NET** & **CACD** & **Paras** \\ \hline \multirow{8}{*}{_Bulky_} & DEX [21] & 3.25 & 4.63 & - & 138 M \\  & DEX * [21] & 2.68 & 3.09 & 6.52 & 138 M \\  & MV [3] & 2.41 & 4.10 & - & 138 M \\  & MV * [3] & 2.16 & 2.68 & - & 138 M \\  & DDLL-v2 [2] & 1.969 & - & - & 138 M \\  & FP-Age [49] & 2.04 & 5.60 & 5.60 & 138 M \\  & FP-Age * [49] & **1.90** & 4.68 & **4.33** & 138 M \\  & DRF [6] & 2.80 & 3.47 & 5.63 & - \\  & PML [50] & 2.31 & **2.16** & - & - \\  & JREAE [13] & 2.71 & 3.390 & 4.596 & - \\  & MWR [18] & 2.13 & - & 5.68 & - \\ \cline{2-5}  & FW-GPL [**Ours**] & 2.71 & 4.27 & - & 138 M \\  & FW-GPL * [**Ours**] & 2.24 & 2.73 & 6.10 & 138 M \\ \hline \multirow{8}{*}{_Compact_} & ORCNN [3] & 3.27 & 6.44 & - & 479.7 K \\  & MRCNN [3] & 3.42 & - & - & 479.7 K \\ \cline{1-1}  & SSR [5] & 3.16 & - & - & 40.9 K \\ \cline{1-1}  & C3 AE [4] & 2.78 & 4.09 & - & 39.7 K \\ \cline{1-1}  & C3 AE * [4] & 2.75 & 2.95 & - & 39.7 K \\ \cline{1-1}  & **AVDL * [10]** & 2.37 & 2.32 & - & 11 M \\ \cline{1-1}  & **MWR [18]** & **2.00** & **2.23** & - & - \\ \cline{1-1} \cline{2-5}  & **FW-GPL[Ours**]** & 2.72 & 3.71 & - & 40.9 K \\ \hline \hline \end{tabular}
\end{table}
Table 5: In terms of MAEs, our approach is compared with different SOTA methods. (* indicates the model was pre-trained on the IMDB-WIKI dataset.)Compared with some models that use special loss functions, we find that our FW-GPL, in particular, achieves competitive results compared with most SOTA methods, for example, MV Loss [3], SSR [5], and C3 AE [4]. This is because the fuzzy window reduces the influence of the conjugation among neighboring ordinal categories, and it does not like DLDL-V2 [2], MV Loss [3], and SSR [5] considering the whole probability, or as with C3 AE [4], focusing only on the two highest output probabilities. The second reason is that the Gaussian processed labels do not need to fit a proper hyperparameter \(\sigma\)[10] that should approximate the true age probability distribution. Compared with our FW-GPL, MWR [18] developed global and local relative ordinal regressors (\(\rho\) regressors) to predict \(\rho\) ranks within the entire and specific rank ranges. Furthermore, MWR first refined an initial search window, iteratively moved it by selecting two reference instances, and, lastly, estimated the \(\rho\) rank within the window.