A method for identifying a user using an image of an object of the user that has a biometric characteristic of the user, wherein the object is one of a palm of the hand of the user, a face of the user, an eye of the user, a bottom of a foot of the user, the method comprising: obtaining, by an optical sensor of a mobile device, the image of the object; providing the image to a neural network; processing the image by the neural network, thereby identifying both, the position of the object and the object in the image; wherein processing the image by the neural network comprises processing, by a first layer of the neural network, the input to create a first intermediate output and processing, by each following layer the output of the preceding layer, wherein each layer of the neural network is a depthwise separable convolution consisting, in the processing order of the input within the layer, a depthwise convolutional layer, a first batch normalizer, a first rectified linear unit, a pointwise convolutional layer, a second batch normalizer and a second rectified linear unit; wherein, by processing the input using the plurality of layers, the neural network obtains, as an output, an identification of the object and the location of the object within the image; wherein identifying the position of the object comprises separating the image into a grid comprising QxR grid cells, each grid cell comprising at least one bounding box, wherein a position of the bounding box is calculated relative to a center of the grid cell in two dimensions and geometrical characteristics of the bounding box are calculated and comprise a height and a width of the bounding box, wherein, further, a probability of the object being within the bounding box is calculated and associated with each bounding box, and wherein the output of the neural network comprises a tensor T of dimension Q × R × B × A, where A is the number of different bounding boxes in each grid cell and B is a vector associated with each bounding box having the dimension 5 and being represented as B = x − position of bounding box y − position of bounding box width of bounding box heigth of bounding box probability; extracting, from the identified object, the biometric characteristic; storing the biometric characteristic in a storage device and/or providing at least the biometric characteristic as input to an identification means, comprising processing the input in order to determine whether the biometric characteristic identifies the user; wherein each step of the method is performed on the mobile device and wherein the mobile device is embodied as a smartphone; wherein the neural network is a trained neural network and wherein the training is performed before the mobile device is equipped with an application or other program that can perform the method. obtaining, by an optical sensor of a mobile device, the image of the object; providing the image to a neural network; processing the image by the neural network, thereby identifying both, the position of the object and the object in the image; wherein processing the image by the neural network comprises processing, by a first layer of the neural network, the input to create a first intermediate output and processing, by each following layer the output of the preceding layer, wherein each layer of the neural network is a depthwise separable convolution consisting, in the processing order of the input within the layer, a depthwise convolutional layer, a first batch normalizer, a first rectified linear unit, a pointwise convolutional layer, a second batch normalizer and a second rectified linear unit; wherein, by processing the input using the plurality of layers, the neural network obtains, as an output, an identification of the object and the location of the object within the image; wherein identifying the position of the object comprises separating the image into a grid comprising QxR grid cells, each grid cell comprising at least one bounding box, wherein a position of the bounding box is calculated relative to a center of the grid cell in two dimensions and geometrical characteristics of the bounding box are calculated and comprise a height and a width of the bounding box, wherein, further, a probability of the object being within the bounding box is calculated and associated with each bounding box, and wherein the output of the neural network comprises a tensor T of dimension Q × R × B × A, where A is the number of different bounding boxes in each grid cell and B is a vector associated with each bounding box having the dimension 5 and being represented as B = x − position of bounding box y − position of bounding box width of bounding box heigth of bounding box probability; extracting, from the identified object, the biometric characteristic; storing the biometric characteristic in a storage device and/or providing at least the biometric characteristic as input to an identification means, comprising processing the input in order to determine whether the biometric characteristic identifies the user; wherein each step of the method is performed on the mobile device and wherein the mobile device is embodied as a smartphone; wherein the neural network is a trained neural network and wherein the training is performed before the mobile device is equipped with an application or other program that can perform the method.