A method, performed on a mobile device, for identifying a user using an image of an object of the user that has a biometric characteristic of the user, like a fingerprint or a set of fingerprints of fingertips, the method comprising: obtaining, by an optical sensor of the 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; 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 processing the image as input 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 the neural network performs a depthwise separable convolution and comprises, in the processing order, of an input layer, a depthwise convolutional layer, a first batch normalizer, a first rectified linear unit, a pointwise convolutional layer, a second batch normalizer, a second rectified linear unit and an output layer; 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 Q × R grid cells, wherein at least one bounding box is created within each grid cell, the bounding box having a predetermined position within the grid cell and predetermined geometrical characteristics, wherein creating the output further comprises modifying the position and the geometrical characteristics of the bounding box to obtain a resulting bounding box, wherein the resulting bounding box is the bounding box having a resulting position and resulting geometrical characteristics that most closely match the location of the object. obtaining, by an optical sensor of the 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; 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 processing the image as input 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 the neural network performs a depthwise separable convolution and comprises, in the processing order, of an input layer, a depthwise convolutional layer, a first batch normalizer, a first rectified linear unit, a pointwise convolutional layer, a second batch normalizer, a second rectified linear unit and an output layer; 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 Q × R grid cells, wherein at least one bounding box is created within each grid cell, the bounding box having a predetermined position within the grid cell and predetermined geometrical characteristics, wherein creating the output further comprises modifying the position and the geometrical characteristics of the bounding box to obtain a resulting bounding box, wherein the resulting bounding box is the bounding box having a resulting position and resulting geometrical characteristics that most closely match the location of the object.