# Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study
## 2 CNN Models for Robot Face Recognition  
The evaluation of convolution neural network (CNN) tremendously changed the researchers thought process towards the applications of computer vision like object recognition, semantic segmentation, image fusion and so on. It play key role in machine learning or deep learning algorithms. The major difference between these two is in their structure. In machine learning architecture, features are extracted with various CNN layers and classification is done with other classification algorithm whereas in deep-learning both feature extraction and classification are available in same architecture [10]. The artificial neural network (ANN) are feedback networks and CNN are feed-forward networks which are inspired by the process of neurons in human brain. It (ANN's) has mostly one input, output and one hidden layers depends on the problem one can increase the hidden layers. In general, a CNN has a convolution layer, an activation layer, a pooling layer, and a fully connected layer. Convolution layer has a number of filters of different sizes (such as 3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7) to perform convolution operation on input image aiming to extract the image features. To detect features, these filters are sliding over the image and perform a dot product, and these features are given to an activation layer. In the activation layer, activation function decides the outcome. The main activation functions are: binary step, linear activation, Sigmoid, and Rectified linear unit (ReLu). Especially in our article, we preferred ReLu activation function and its respective neuron outcome becomes one if summed multiplied inputs and weights exceeds certain threshold value or else it becomes zero. In certain region it obeys the linearity rule between input and output of respective neuron. Once features are extracted with various kernels, for dimensionality reduction, outcome of CNN are passed through pooling layer. In present research, there are various ways pool the layer. Average pooling is one simple approach in which average pooling is a combination of the pooling layer and the pooling layer. The pooling layer is a combination of pooling layer and pooling layer. of feature map is consider, in max pooling, consider maximum among the feature map. Finally, a fully connected layer resulting from the pooled feature map is converted to a single long continuous linear vector as shown in Figure 1. In what follows, we discuss the main CNN models including LeNet, Alexnet, VGG16 Net, GoogLeNet, ResNet, DenseNet, MobileFaceNet, EffNet, and ShuffleNet. Convolutional neural networks (CNN or ConvNet) present a category of deep neural networks, which are most often used in visual image analysis [10]. The model of connectivity between the CNN neurons is inspired from the organization of the animal visual cortex. A CNN is generally composed of a convolution layer, an activation layer, a pooling layer, and a fully connected layer. The convolution layer includes a set of filters of different sizes (e.g., \(3\times 3\), \(5\times 5\), \(7\times 7\)). These filters are applied in a convolution operation on the input image in order to extract the image features. To detect these features, the input image is scanned by these filters and a scalar product is performed. The obtained features presents the input of the activation layer which decides on the outcome. The main activation functions are: binary step, linear activation, Sigmoid and ReLu. We opted, in this work, for a rectified linear unit transformation function (ReLu). The ReLu transformation function activates a node only if the input is greater than a certain threshold. If the input is less than zero, the output is zero. On the other hand, when the input exceeds a certain threshold, the activation function becomes a linear relationship with the independent variable. Then, the rectified features go through a pooling layer. Pooling is a downsampling operation reducing the dimensionality of the feature map.