ZAFAR ASOODEH, AMIR (2015) FACE AND FRAME CLASSIFICATION USING GEOMETRIC FEATURES FOR A DATA-DRIVEN EYEGLASS RECOMMENDATION SYSTEM. Masters thesis, Concordia University.
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Abstract
Recommending glasses based on face and frame features is the main issue of this thesis. In this work we present an automatic shape extraction and classification method for face and eyeglass frame shapes. Our novel frame shape extraction algorithm can extract the polygonal shape of the frame accurately and reliably even for reflective sunglasses and thin metal frames. Additionally, we identify the key geometric features that can di↵erentiate reliably the shape classes and we integrate them into a supervised learning technique for face and frame shape classification. Finally, we incorporate the shape extraction and classification algorithms into a practical data-driven frame recommendation system that we validate empirically with a user study. 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 90% 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.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | ZAFAR ASOODEH, AMIR |
| Institution: | Concordia University |
| Degree Name: | M. Comp. Sc. |
| Program: | Computer Science |
| Date: | 19 January 2015 |
| Thesis Supervisor(s): | POPA, TIBERIU |
| ID Code: | 979910 |
| Deposited By: | AMIR ZAFAR ASOODEH |
| Deposited On: | 13 Jul 2015 14:22 |
| Last Modified: | 18 Jul 2019 15:11 |
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