A computer-implemented method for determining a body fat measurement from multiple two-dimensional images comprising multiple different views of a person, the method comprising: receiving, by an input handling component (1302, 1402) of the system, the multiple two-dimensional images; processing the images by one or more neural networks (1307, 1407-1, 1407-2) in a modelling component (1304, 1404) of the system to determine a single body fat measurement value representative of the body fat of the person represented in the images, wherein each of the one or more neural networks (1307, 1407-1, 1407-2) is a modified residual neural network and processing each neural network comprises: extracting features learned in a plurality of bottlenecks of the modified residual neural network by generating the extracted features (1810-2 to 1810-5) from down-sampled feature representations (1808-1 to 1808-5) of all or fewer than all of the bottlenecks of the modified residual neural network by averaging outputs from the down-sampled feature representation across spatial dimensions, wherein each of the plurality of bottlenecks (1807-1 to 1807-5) reduces spatial resolution to generate a respective down-sampled feature representation (1808-1 to 1808-5), each bottle neck being a stack of deep-learning units, each deep-learning unit being one of: a convolution layer, a non-linear activation function, a pooling operation and batch normalization, concatenating the extracted features with an output of the neural network, applying a linear function to the concatenated features to determine a body fat measurement representation comprising a plurality of elements, Vi, in which each element denotes a probability of the body fat measurement taking a value, and generating a body fat measurement value by taking a probability weighted average of the body fat measurement values. receiving, by an input handling component (1302, 1402) of the system, the multiple two-dimensional images; processing the images by one or more neural networks (1307, 1407-1, 1407-2) in a modelling component (1304, 1404) of the system to determine a single body fat measurement value representative of the body fat of the person represented in the images, wherein each of the one or more neural networks (1307, 1407-1, 1407-2) is a modified residual neural network and processing each neural network comprises: extracting features learned in a plurality of bottlenecks of the modified residual neural network by generating the extracted features (1810-2 to 1810-5) from down-sampled feature representations (1808-1 to 1808-5) of all or fewer than all of the bottlenecks of the modified residual neural network by averaging outputs from the down-sampled feature representation across spatial dimensions, wherein each of the plurality of bottlenecks (1807-1 to 1807-5) reduces spatial resolution to generate a respective down-sampled feature representation (1808-1 to 1808-5), each bottle neck being a stack of deep-learning units, each deep-learning unit being one of: a convolution layer, a non-linear activation function, a pooling operation and batch normalization, concatenating the extracted features with an output of the neural network, applying a linear function to the concatenated features to determine a body fat measurement representation comprising a plurality of elements, Vi, in which each element denotes a probability of the body fat measurement taking a value, and generating a body fat measurement value by taking a probability weighted average of the body fat measurement values. extracting features learned in a plurality of bottlenecks of the modified residual neural network by generating the extracted features (1810-2 to 1810-5) from down-sampled feature representations (1808-1 to 1808-5) of all or fewer than all of the bottlenecks of the modified residual neural network by averaging outputs from the down-sampled feature representation across spatial dimensions, wherein each of the plurality of bottlenecks (1807-1 to 1807-5) reduces spatial resolution to generate a respective down-sampled feature representation (1808-1 to 1808-5), each bottle neck being a stack of deep-learning units, each deep-learning unit being one of: a convolution layer, a non-linear activation function, a pooling operation and batch normalization, concatenating the extracted features with an output of the neural network, applying a linear function to the concatenated features to determine a body fat measurement representation comprising a plurality of elements, Vi, in which each element denotes a probability of the body fat measurement taking a value, and generating a body fat measurement value by taking a probability weighted average of the body fat measurement values.