Abstract: A computer-implemented method is provided for measuring optometric values and for manipulating a glasses frame remotely in order to manufacture lenses and provide customized glasses for a person. The method includes providing aesthetic indications on the correct position of the glasses in relation to the person's head, and assessing a degree of comfort in relation to the possibility of deformation of the arms and frame.

Abstract: A modelling method for making a virtual model of a user's head includes operations of: acquiring a plurality of depth images of a user's head in different head poses; processing the acquired depth images; localising on the two-dimensional images first marker points corresponding to characteristics of the head; estimating the head by estimating the yaw, pitch and roll angles of the head and the positioning of the head along three axes of a reference system of Cartesian axes for each of the two-dimensional images of the depth images, performing a transformation of the head pose; projecting the first marker points located in the respective cloud of points; associating iteratively in pairs the clouds of points until obtaining a cloud of synthesis points; aligning the cloud of synthesis points; and deforming the statistical head model by means of a minimization algorithm of a predefined cost function.

Abstract: A system performs an interpupillary distance estimation method, which includes capturing a plurality of depth images of a user’s face where each depth image comprises a 2D image and a 2D map of data representative of the distances of each pixel from an observation point. For each of the captured depth images, the system processes the captured depth image by correcting the alignment between the 2D map of data and the 2D image, locates two first marker points corresponding to the pupils on the 2D image, and obtains the spatial coordinates in metric units of the first marker points from the 2D data map. The system further determines an initial estimate of the interpupillary distance by calculating the distance between the first marker points, determines a second estimate of the interpupillary distance, and calculates the final estimate of the interpupillary distance based on the first estimates or the second estimates.

Abstract: An interpupillary distance estimation method is implementable by an electronic computer and includes the operations of acquiring at least one 2D image of a user's face, locating on the 2D image two reference points corresponding to the pupils of the user, and measuring the distance in pixels between the two reference points. The method further includes measuring the diameter in pixels of the user's iris, and calculating the pixel-to-millimetre conversion ratio between a predetermined iris diameter expressed in metric units and the iris diameter measured in pixels. The predetermined pupil diameter is set equal to the value of the iris diameter most widespread in the world population according to data contained in an anthropometric database. A first estimation of the interpupillary distance is determined by multiplying the distance in pixels between the two reference points corresponding to the user's pupils by the pixel-to-millimetre ratio calculated previously.

Abstract: A computer-implemented method is provided for measuring optometric values and for manipulating a glasses frame remotely in order to manufacture lenses and provide customized glasses for a person. The method includes providing aesthetic indications on the correct position of the glasses in relation to the person's head, and assessing a degree of comfort in relation to the possibility of deformation of the arms and frame.

Abstract: A product recommendation method includes an indexing step implementing a Bayesian network capable of creating direct matches between each respective product of a product catalog and an ideal user whose characteristics are the most likely to be suitable for the respective products. The indexing step includes inputting a first descriptor vector of a product into the Bayesian network to obtain the user-characteristics most likely to be suitable for the product. The product recommendation method further includes a refining process which identifies determinants associated with nodes and conditional probabilities of the Bayesian network to minimize errors at the nodes to which the arcs of the Bayesian network point.