תקציר
We propose a novel approach to template based face recognition. Our dual goal is to both increase recognition accuracy and reduce the computational and storage costs of template matching. To do this, we leverage on an approach which was proven effective in many other domains, but, to our knowledge, never fully explored for face images: average pooling of face photos. We show how (and why!) the space of a template's images can be partitioned and then pooled based on image quality and head pose and the effect this has on accuracy and template size. We perform extensive tests on the IJB-A and Janus CS2 template based face identification and verification benchmarks. These show that not only does our approach outperform published state of the art despite requiring far fewer cross template comparisons, but also, surprisingly, that image pooling performs on par with deep feature pooling.
שפה מקורית | אנגלית |
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כותר פרסום המארח | Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 |
מוציא לאור | IEEE Computer Society |
עמודים | 127-135 |
מספר עמודים | 9 |
מסת"ב (אלקטרוני) | 9781467388504 |
מזהי עצם דיגיטלי (DOIs) | |
סטטוס פרסום | פורסם - 16 דצמ׳ 2016 |
אירוע | 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 - Las Vegas, ארצות הברית משך הזמן: 26 יוני 2016 → 1 יולי 2016 |
סדרות פרסומים
שם | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
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ISSN (מודפס) | 2160-7508 |
ISSN (אלקטרוני) | 2160-7516 |
כנס
כנס | 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 |
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מדינה/אזור | ארצות הברית |
עיר | Las Vegas |
תקופה | 26/06/16 → 1/07/16 |
הערה ביבליוגרפית
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