What Data Do Beauty Filters Need from Us and What Happens to It Afterwards?Ī photograph or video on which beauty filters can be applied is used as a basis. Celebrities like Instagram star Kylie Jenner have been doing this for some time – she developed a beauty filter for her make-up brand in 2018 that allows users to test various colours of the lipsticks she advertises directly on their own images. Since August 2019, Spark AR has made it possible for private individuals and companies to have filters produced using their services. The Spark AR company develops the bulk of existing beauty filters (or commissions others to develop them). After further development, it achieved international success in 2016. It was released by the Lighttricks company and was first put to market in 2013. The most successful photo editing app with beauty filters is Facetune. Many smartphone suppliers, including Samsung, Huawei, Xiao Mi, and Apple, also integrate beauty filters as standard features in their cameras. The best-known apps with beauty filters are Aviary, Facetune, Perfect365 and Snapseed. A beauty filter must thus first understand the contours, proportions and individual attributes of the person in the picture before it can superimpose a standardised filter function that fits exactly over the photograph. This can only happen after the machine has ‘recognised’ the face and/or the body by comparing it to data sets from other photographs. When a specific beauty filter is selected, the features that have been programmed are applied to the image of a person the user has selected, such as a filter for a smooth complexion achieved by blurring areas of skin in the photograph. This enables the machine to ‘learn’ and make decisions, though it can also question and change these decisions if necessary. Deep learning technology is used to train an artificial neuronal network, which, like the structures of the human brain, processes information from image data sets and thereby creates more and more new connections between them. Beauty filters are based on machine learning.
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