![]() One useful way to think of the portraits is as ‘fashion interested individuals with an iPhone.’ These portrait selfies were collected only for analytics. This set is not representative of the distribution of humans across ages, gender, geographies, and races. Through the usage of our app, we have over 100K portrait selfies of humans. We have addressed this in the analysis shown below and plan on releasing a more effective app in the near future. There are challenges in this version of the app, most importantly a lack of repeatability in seasonal assignment due to variations in the lighting and composition of the portrait selfie. This is useful in a location such as a clothing store.Īugmented Reality in a Clothing Store for an Autumn ![]() Further, we showed that person their seasonal palette and also provided them augmented reality capabilities - such as a live video stream from their camera’s phone showing good, and bad colors highlighted. So a group of us researched, designed and deployed an iOS app that did just that, i.e., take a selfie portrait and identify that person’s season. Given the prevalence of smartphones with cameras - isn’t this a natural area to attempt to automatically identify a person’s season? Interestingly, these practitioners often disagree into which season an image is to place, especially in edge cases. There is guidance for users to self identify, though historically users have been placed into their season by expert practitioners. The seasonal aspect relates to placing all humans into one of four segments, named after the seasons. Seasonal Color Analysis (SCA) is based on the concept that harmonious colors will enhance the natural beauty of the individual. Further work is also taking us to revisit personal color analysis - the creation of personalized palettes based on color harmonies from an individual. With a new, science-based segmentation there is a solid base on which to better view a data approach to market segmentation for fashion design, marketing, and retailing. While there is clearly nice clustering of two clusters and four clusters - these don’t correlate at all to the seasonal descriptions. ![]() (By ‘portrait selfie’ I explicitly mean a portrait at high resolution - we need lots of pixels on the iris!) We then apply cluster analysis techniques to group selfies together based on the similarity of their body part colors. Using over 100,000 ‘portrait selfies’ collected from our current iPhone application (All Eyes on Hue) we break each selfie into key body parts - skin, hair, eyes, and lips.
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