Hand Gesture Recognition Without Image Rotation

To investigate hand gesture recognition performance without image rotation tech- nique, each database is partitioned into training and testing sets. Randomly selected 50% image of all gestures are used for training and the remaining 50% image are utilized for testing. The experiments are separately conducted on three distinct databases uti- lizing LCS [16], block-based [17] and proposed combined feature sets with MLP-BP-NN classifier to evaluate the recognition performance for each feature set. The comparative performances of gesture recognition without image rotation technique using LCS [16], block-based [17] and proposed combined feature sets are tabulated in Table 2.1. The comparative results indicate that the average accuracy, sensitivity, positive predictiv- ity and specificity of hand gesture recognition without image rotation technique using proposed combined feature set are 99.94%, 99.20%, 99.24% and 99.97% respectively for Database I, 98.82%, 85.83%, 86.15% and 99.38% respectively for Database II and

99.74%, 96.92%, 96.99% and 99.87% respectively for Database III. On the other hand, the average accuracy, sensitivity, positive predictivity and specificity of hand gesture recognition without image rotation technique using LCS features are 99.90%, 98.80%, 98.89% and 99.95% respectively for Database I, 98.76%, 85.17%, 85.48% and 99.36% respectively for Database II and 99.67%, 96.08%, 96.14% and 99.83% respectively for Database III, and using block-based features are 99.78%, 97.20%, 97.25% and 99.88% re- spectively for Database I, 97.55%, 70.58%, 70.37% and 98.72% respectively for Database II and 99.31%, 91.67%, 91.68% and 99.64% respectively for Database III. In this exper-

Table 2.1: Comparative performances of hand gesture recognition without im- age rotation technique using LCS, block-based and proposed combined feature sets for three distinct databases.

Database Feature set Acc (%) Sen (%) Ppr (%) Spe (%)

Database I LCS [16] 99.90 98.80 98.89 99.95 Block-based [17] 99.78 97.20 97.25 99.88 Proposed combined 99.94 99.20 99.24 99.97 Database II LCS [16] 98.76 85.17 85.48 99.36 Block-based [17] 97.55 70.58 70.37 98.72 Proposed combined 98.82 85.83 86.15 99.38 Database III LCS [16] 99.67 96.08 96.14 99.83 Block-based [17] 99.31 91.67 91.68 99.64 Proposed combined 99.74 96.92 96.99 99.87

iment, a single hidden layer MLP-BP-NN is used as a classifier to recognize the hand gesture images based on their feature sets. A neural network with a small number of neurons may not be sufficiently powerful to model a complex function [83]. On the other hand, a neural network with too many neurons may lead to overfitting of the training sets and lose its ability to generalize which is the primary desired characteristic of a neural network [83]. The recognition performance of the classifier is dependent on the number of hidden nodes in hidden layer [2]. However, there is no technique available for the exact selection of hidden nodes in a MLP-BP-NN [65]. In this work, the numbers of hidden nodes of the MLP-BP-NN are empirically selected based on best recognition performance. As an example, for Database II, the variation of gesture recognition per-

2.4 Performance Evaluation

formance with respect to the number of hidden nodes using proposed combined feature set is shown in Figure 2.9. It is observed from Figure 2.9 that the best recognition performance is achieved when the number of hidden nodes in single hidden layer MLP- BP-NN classifier is 200. The number of input and output nodes of the MLP-BP-NN are selected based on the length of applied feature set and number of hand gesture classes respectively. 100 150 200 250 300 350 82 82.5 83 83.5 84 84.5 85 85.5 86

Number of hidden nodes

Recognition Sensitivity (%)

Figure 2.9: The performances of gesture recognition using proposed combined feature set for Database II when different number of hidden nodes are used in a single hidden layer MLP-BP-NN.

The performance of hand gesture recognition without image rotation technique using LCS, block-based and proposed combined features are further analyzed in terms of receiver operating characteristic (ROC) graph [84]. The relationship between sensitivity and specificity are described by the receiver operating characteristic (ROC) graph which alleviates improved analysis in terms of the recognition performance of a recognition technique. On an ROC graph, false positive rate (FPR) (or 1-specificity) and true positive rate (TPR) (or sensitivity) are separately plotted on the X and Y axis. The TPR and the FPR are separately defined by (2.26) and (2.29) respectively.

F pr = F P

For accurate recognition, TPR=1 and FPR=0 corresponds to the upper left corner of the ROC graph. Therefore, in ROC graph, the combination of TPR-FPR is considered better when it is located nearer to the upper left corner. Figure2.10shows ROC graphs of hand gesture recognition without image rotation technique using block-based, LCS and proposed combined features for three district databases. It is observed that recog- nition without image rotation technique using proposed combined feature set provides a higher TPR but lower FPR compared to LCS and block-based feature sets, individually for all three databases. Therefore, from Table 2.1 and Figure 2.10, it is observed that

4 6 8 10 12 x 10−4 0.96 0.97 0.98 0.99 FPR TPR (a) 6 8 10 12 x 10−3 0.7 0.75 0.8 0.85 FPR TPR (b) 1.5 2 2.5 3 3.5 4 x 10−3 0.9 0.92 0.94 0.96 0.98 FPR TPR (c) LCS features Block based features Proposed combined features

Figure 2.10: ROC graphs of hand gesture recognition without image rota- tion technique using LCS, block-based and proposed combined features for (a) Database I, (b) Database II and (c) Database III.

proposed combined feature set offers better recognition performance compared to LCS and block-based features, individually for all three databases.

2.4 Performance Evaluation