Proceedings: GI 2016

Face and Frame Classification using Geometric Features for a Data-driven Frame Recommendation System

Amir Zafar (Concordia University), Tiberiu Popa (Concordia Unniversity)

Proceedings of Graphics Interface 2016: Victoria, British Columbia, Canada, 1-3 June 2016, 183-188

DOI 10.20380/GI2016.23

  • Bibtex

    @inproceedings{Zafar:2016:10.20380/GI2016.23,
    author = {Zafar, Amir and Popa, Tiberiu},
    title = {Face and Frame Classification using Geometric Features for a Data-driven Frame Recommendation System},
    booktitle = {Proceedings of Graphics Interface 2016},
    series = {GI 2016},
    year = {2016},
    issn = {0713-5424},
    isbn = {978-0-9947868-1-4},
    location = {Victoria, British Columbia, Canada},
    pages = {183--188},
    numpages = {6},
    doi = {10.20380/GI2016.23},
    publisher = {Canadian Human-Computer Communications Society / Soci{\'e}t{\'e} canadienne du dialogue humain-machine},
    }

Abstract

In this work we present an automatic shape extraction and classification method for face and eye-ware shapes. Our novel eye-ware shape extraction algorithm can extract the polygonal shape of eyeware accurately and reliably even for reflective sun-glasses and thin metal frames. Additionally, we identify key geometric features that can differentiate reliably the shape classes and we integrate them into a supervised learning technique for face and eye-ware shape classification. Finally, we incorporate the shape extraction and classification algorithms into a practical data-driven eye-ware recommendation system that we validate empirically with a user study.