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Master Thesis Defense: Amir Zafar Asoodeh

January 19, 2015

Speaker: Amir Zafar Asoodeh

Supervisor: Dr. T Popa

Examining Committee: Drs. S. P. Mudur, C. Y. Suen, E. Shihab (Chair) 

Title: Face and Frame Classification Using Geometric Features for a Data-driven Eyeglass                                   Recommendation System

Date: Monday, January 19, 2015

Time: 11:30 a.m.

Place: EV 3.309

ABSTRACT

Recommending glasses based on face and frame features is the main issue of this thesis. In this work we present an automatic classification method for face and eyeglass types that we incorporated in a data-driven eyeglass recommendation system. Using a supervised learning technique, we identified the geometric discriminatory features that can be used to classify both the face type and the eyeglass type form a single photograph. Our classification method reaches near 100% accuracy. We ran this classification on over 200 photographs and we surveyed 100 people on the compatibility between face and eyeglasses. Using this data we created an eyeglass recommendation system that we have validated experimentally.




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