תקציר
We introduce our method and system for face recognition using multiple pose-aware deep learning models. In our representation, a face image is processed by several pose-specific deep convolutional neural network (CNN) models to generate multiple pose-specific features. 3D rendering is used to generate multiple face poses from the input image. Sensitivity of the recognition system to pose variations is reduced since we use an ensemble of pose-specific CNN features. The paper presents extensive experimental results on the effect of landmark detection, CNN layer selection and pose model selection on the performance of the recognition pipeline. Our novel representation achieves better results than the state-of-the-art on IARPA's CS2 and NIST's IJB-A in both verification and identification (i.e. search) tasks.
שפה מקורית | אנגלית |
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כותר פרסום המארח | 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 |
מוציא לאור | Institute of Electrical and Electronics Engineers Inc. |
עמודים | 1 |
מספר עמודים | 9 |
מסת"ב (אלקטרוני) | 9781509006410 |
מזהי עצם דיגיטלי (DOIs) | |
סטטוס פרסום | פורסם - מרץ 2016 |
אירוע | IEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, ארצות הברית משך הזמן: 7 מרץ 2016 → 10 מרץ 2016 |
סדרות פרסומים
שם | 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 |
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כנס
כנס | IEEE Winter Conference on Applications of Computer Vision, WACV 2016 |
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מדינה/אזור | ארצות הברית |
עיר | Lake Placid |
תקופה | 7/03/16 → 10/03/16 |
הערה ביבליוגרפית
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