# Extending Appearance Based Gait Recognition with Depth Data
## 5 Experimental Validation
### Results  
The classification algorithms k-nearest neighbors (kNN), Support Vector classification (SVC), Random forest classification (RFC), and Gaussian Naive Bayes (GNB) were tested on previously described height and gait features. The heights from the _Real_ (Figure 10) height distribution and gait features after dimensionality reduction (Figure 4) were used, as well as the integrated features of the HGEI-i method. The performance of the considered classification algorithms with gait featuresis shown in Table 2 using the Accuracy, Precision, Recall, F1 Score, and Kappa metrics. The kNN algorithm slightly outperformed the other classification algorithms and was, therefore, selected for the classification step in the methods presented hereafter. The effects that height distribution and size of a population have on the recognition accuracy of the classification that uses the height feature are visible in Figure 11. The method performs poorly for medium and large population sizes. As expected, the worst performance for the Height method is for the population with _Similar_ height distribution, containing individuals with similar heights, while, for larger population sizes, the best is for _Age_ height distribution, which contains individuals of different ages with a varied height distribution. With average recognition accuracy of 40% for _small_, 14% for _medium_, and 8% for _large_ populations, the Height method, using only the height feature for classification, significantly underperforms the other methods, irrespective of the particular population height distribution. In Table 3, the Accuracy, F1 score, and Kappa performance indexes for the above mentioned approaches (Height, GEI, HGEI-i, HGEI-f, BGEI, HBGEI-i, and HBGEI-f) evaluated on TUM GAID dataset with kNN classification algorithm using the _normal_, _backpack_, _shoes_, _time normal_, _time backpack_, and _time shoes_ variations are presented for _small_, _medium_, and _large_ populations, respectively. The _Real_ height distribution was used. For comparison, Table 4 contains the same results in the case of _Similar_ height distribution. GEI and BGEI methods have similar recognition performance. Considering the average recognition accuracy for _small_, _medium_, and _large_ populations, BGEI consistently slightly underperforms GEI. The performance of GEI and BGEI methods, trained using samples from _normal_ variation, slightly decreases for _backpack_ and _shoes_ variations. The decrease in recognition accuracy is considerable when \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1** & **Kappa** \\ \hline k-nearest neighbors (kNN) & 0.97 & 0.97 & 0.97 & 0.96 & 0.97 \\ Support vector classification (SVC) & 0.85 & 0.9 & 0.85 & 0.87 & 0.85 \\ Random forest classification (RFC) & 0.95 & 0.96 & 0.95 & 0.94 & 0.95 \\ Gaussian Naive Bayes (GNB) & 0.81 & 0.84 & 0.81 & 0.8 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of classification algorithms using gait features after dimensionality reduction. Figure 11: Height feature classification performance for five different height distributions and three different population sizes. samples of individuals taken on different occasions (_time normal_, _time backpack_, and _time shoes_) are used. This is true across all the tested scenarios, i.e., for small and large population sizes, and all variations. The average decrease in recognition accuracy is 67% for _time normal_, 64% for _time backpack_, and 63% for _time shoes_ variants.