## 2 Related Work
Sigmoid was selected as the activating function of the hidden and output layer. Images of people were used as input data to this neural network. The data on which the neural network was trained consisted of 270 images and 30 people of different ages, ethnicity and gender. The results of this model were measured by classification accuracy on test data, resulting in 83.33% success [25]. For the individual phases of the recognition process (and thus also the classifications), the so-called convolutional neural networks are used [26]. These are used, for example, for object detection, detection and classification of emotions from a person's voice or face, etc. Several main types of such networks are known, among the best and most important ones are:  
* [noitemsep,nolistsep]
* LeNet;
* AlexNet;
* ZfNet;
* VGG;
* GoogleNet [27]. **LeNet** is the first convolutional neural network ever created. It was created by LeCun, and the most recent version of this network is currently the LeNet-5. It is used to recognize handwritten or typewritten numbers. It consists of five convolutional and pooling layers that alternate, followed by two fully connected layers [28]. **AlexNet** is a convolutional neural network for object detection. This net consists of eight layers with weights. The first five are convolutional, and the rest are fully connected layers. The network maximizes multinomial logistic regression [29]. **ZfNet** is used to detect objects, similar to AlexNet. It is a combination of the first two mentioned networks with modifications. Its principle is the use of standard fully controlled convolution models. These networks map a color 2D image. Each layer consists of a convolution of the output of the previous layer. For the first layer, a set of learned filters is used [30]. **VGG** is a convolutional network used to detect objects. It is based on AlexNet with a different configuration. This network is used on a much larger pool of samples. The network configuration is not fixed, although each of these networks consists of several convolution layers, followed by three fully connected layers. The last layer is the so-called soft-max layer [31]. **GoogleNet** is another object detection network. It is currently one of the most successful networks, based on the LeNet network. It uses convolutional blocks for classification. It uses average pooling instead of completely connected layers. It consists of a total of 22 layers [32]. Ref. [33] described the development of a convolutional neural network for facial classification. The formed neural network had an input, two convolutional, a coupling and a dense output layer. The model was trained on the \"Yale Face Database\" containing 165 single-channel images of 15 people. Each person was represented by 11 images. The input images were chosen with a size of 64 \(\times\) 64 pixels. The first convolution layer contained six characteristic tables with a dimension of 58 \(\times\) 58, and the individual values in the characteristic tables were calculated from the partial data of the input image with a size of 7 \(\times\) 7 pixels. The second merging layer contained six characteristic tables with a dimension of 29 \(\times\) 29, and the partial data were selected with a size of 2 \(\times\) 2 pixels. The third convolution layer contained 16 characteristic tables with a size of 22 \(\times\) 22 pixels, the values of which were calculated from partial data with a size of 8 \(\times\) 8. The fourth merging layer contained 16 characteristic tables of size 11 \(\times\) 11 with partial data 2 \(\times\) 2. The output layer is dense and classified into 15 different people. Their resulting successful classification value was 88.88% on training data and 80% on test data. Based on this experiment, it can be stated that both the application of the forward and convolutional networks provide a classification success of over 80%, which is normally considered an effective use of the classification algorithm.