Online Dating Analytics 4Quant

Modern dating platforms have drastically increased the number of possibilities for modern singles. With this great selection comes, however, the paradox of choice which causes a plethora to quickly transform from an opportunity to a burden. While many sites provide specialized matching algorithms based on your shared like of cats or affinity for Pearl Jam songs, but defining physical characteristics are completely left out. Occasionally self-descriptions are available but are not usually reliable. Endless swiping through images and profiles to find the needles in the haystack is an exhausting process.

2 Solution: Image Query and Analysis Engine

Within each face are millions of possible metrics ranging from nostril size to eye spacing which collectively fit together to define your own version of beauty. Quantitative image analytics can be used to extract these metrics from millions of images in seconds and help filter out the riff-raff and keep only the desired results.

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SELECT Winking=TRUE AND HAIR_COLOR = "black"

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2.1 Real-time More importantly than a single query, is the ability to perform queries on complex datasets in real-time and have the processing distributed over a number of machines. \(\rightarrow\) \(\rightarrow\) \(\rightarrow\)

2.2 How? The first question is how the data can be processed. The basic work is done by a simple workflow on top of our Spark Image Layer. This abstracts away the complexities of cloud computing and distributed analysis. You focus only on the core task of image processing. The true value of such a scalable system is not in the single analysis, but in the ability to analyze hundreds, thousands, and even millions of samples at the same time. With cloud-integration and Big Data-based frameworks, even handling an entire city network with 100s of drones and cameras running continuously is an easy task without worrying about networks, topology, or fault-tolerance.

2.3 What? The images come from one or more dating sites or apps in the form of a real-time stream. The first step is to identify the background and the region for the face inside of the image. The second is to enhance selectively the features in the face itself so they can be more directly quantified and analyzed. The edges can then be used to generate features on the original face to use as the basis for extracting quantitative metrics.

2.4 Possibilities With the ability to segment and analyse faces and features in huge collections of images without processing beforehand, the possibilities are endless. Of particlar interest is the ability to investigate specific metrics as they related to relationship success, for example eye separation and number of likes. SELECT CORR2(left_eye.x-right_eye.x,likes) FROM ( SELECT face.left_eye.x,face.right_eye.x,likes FROM ( SELECT SEGMENT_FACE(profile_image) AS face,likes FROM USER ) )