· A method to produce personalized classification models to automatically review online dating profiles on Tinder is proposed, based on the user's historical preference. The method takes advantage of a FaceNet facial classification model to extract Estimated Reading Time: 10 mins CLASSIFYING ONLINE DATING PROFILES ON TINDER USING FACENET FACIAL EMBEDDINGS Charles F. Jekel (cjekel@blogger.com; cj@blogger.com) and Raphael T. Haftka Department of Mechanical & Aerospace Engineering - University of Florida - Gainesville, FL ABSTRACT A method to produce personalized classification models toCited by: 2 · A method to produce personalized classification models to automatically review online dating profiles on Tinder is proposed, based on the user's historical preference. The method takes advantage of a FaceNet facial classification model to extract features which may be related to facial attractiveness The embeddings from a FaceNet model were
Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings | Papers With Code
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Comments: 6 pages, 7 figures Subjects: Computer Vision and Pattern Recognition cs. CV ; Social and Information Networks cs. SI ; Image and Video Processing eess. IV ; Machine Learning stat. ML Cite as: arXiv CV] or arXiv CV] for this version.
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· A method to produce personalized classification models to automatically review online dating profiles on Tinder is proposed, based on the user's historical preference. The method takes advantage of a FaceNet facial classification model to extract Estimated Reading Time: 10 mins CLASSIFYING ONLINE DATING PROFILES ON TINDER USING FACENET FACIAL EMBEDDINGS Charles F. Jekel (cjekel@blogger.com; cj@blogger.com) and Raphael T. Haftka Department of Mechanical & Aerospace Engineering - University of Florida - Gainesville, FL ABSTRACT A method to produce personalized classification models toCited by: 2 · A method to produce personalized classification models to automatically review online dating profiles on Tinder is proposed, based on the user's historical preference. The method takes advantage of a FaceNet facial classification model to extract features which may be related to facial attractiveness The embeddings from a FaceNet model were
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