## 3 Results
### Creating a New Data Set  
The second experiment creates a new image set to train a deep learning model to detect hands with fingers in images. This application is very useful for enhancing human-robot interactions. Finger detection is an unresolved engineering application for which vision 2D did not find a solution. Finger detection is an extremely difficult task due to the wide variety of shapes they can display in an image. Fingers can be straight or curved, partially occluded, grasping other things, or other hands, and seen from different viewpoints. Research in this area is underway as the use of hands in robot interfaces is a very attractive method for human-computer interaction [32; 33]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**SSC**} & \multicolumn{2}{c}{**FASTER RCNN**} \\  & & **MOBIEIENET** & **RESNET** & **RESNET** & **INCETION** \\ \hline \multirow{4}{*}{training data} & original images & 0.81/0.88 & 0.80/0.74 & 0.85/0.81 & 0.84/0.86 \\  & flat background & 0.82/0.85 & 0.81/0.73 & 0.86/0.89 & 0.82/0.83 \\  & data augmentation & 0.77/0.87 & 0.78/0.75 & 0.80/0.89 & 0.83/0.84 \\  & data augmentation flat background & 0.78/0.86 & 0.77/0.74 & 0.79/0.88 & 0.81/0.82 \\ \hline \multirow{4}{*}{testing data} & original images & 0.41/0.47 & 0.40/0.44 & 0.45/0.51 & 0.44/0.56 \\  & flat background & 0.42/0.45 & 0.41/0.43 & 0.46/0.49 & 0.42/0.53 \\  & data augmentation & 0.47/0.47 & 0.48/0.45 & 0.40/0.49 & 0.43/0.54 \\  & data augmentation flat background & 0.48/0.46 & 0.47/0.44 & 0.49/0.48 & 0.41/0.52 \\ \hline \hline \end{tabular}  
* Model is restored at 2500 epochs trained with different set of images. Original images are images with textured backgrounds. Flat background represents original images but with a removed background. Data augmentation are images with combinations of cats and dogs with occlusions, changing positions and scale. Data augmentation with flat backgrounds are the same images with data augmentation but with a removed background. Testing data are set of images not used in the training step with textured background. Since the pet data set has 37 classes, it is impossible to show precision and recall for each class. Here, precision and recall are mean values of all classes. Results are similar if model training step has images with textured backgrounds or flat backgrounds.  
\end{table} 
Table 1: Detection rates (precision/recall) of deep neural network models 1 with different images data sets of pets. To train a hand detection model, a set of 20,000 images of human fingers are labelled in a few minutes using the proposed fast image-labelling algorithm. Classes are \"one finger\", \"two fingers\" and so on. Figure 5 shows one sample for each class. The first row shows images of hands with a controlled background for which the detection of the hand silhouette was easy. The second row shows masks extracted using the algorithm described in Section 3. The third row shows the resulting images of combining hands with backgrounds to improve the training process. Twenty thousand images are processed with the proposed algorithm for training and 2000 different images, similar to the image in Figure 6, are tested. As before, the aim is to show how to easily create a new data set of labelled images. In this case, FASTER_RCNN_INCEPITION is the model chosen to perform the experiment. Table 2 shows the precision and recall of the model's performance with 2000 testing images of each class. Columns depict the hand rate detection of different subsets. Objects of the \"one finger\" class are classified as \"one finger\", but also in the \"two finger\" class and so on. There is similar occurrence with objects of the \"two fingers\" class and the successive classes. A summary of this information is in the last two rows, which gives the precision and recall of the model with each subset. Precision is tested with 400 images of each class. Recall uses the total number of images that the model classifies under each class.