Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild

Iacopo Masi, Feng Ju Chang, Jongmoo Choi, Shai Harel, Jungyeon Kim, Kanggeon Kim, Jatuporn Leksut, Stephen Rawls, Yue Wu, Tal Hassner, Wael AbdAlmageed, Gerard Medioni, Louis Philippe Morency, Prem Natarajan, Ram Nevatia

פרסום מחקרי: פרסום בכתב עתמאמרביקורת עמיתים


We propose a method designed to push the frontiers of unconstrained face recognition in the wild with an emphasis on extreme out-of-plane pose variations. Existing methods either expect a single model to learn pose invariance by training on massive amounts of data or else normalize images by aligning faces to a single frontal pose. Contrary to these, our method is designed to explicitly tackle pose variations. Our proposed Pose-Aware Models (PAM) process a face image using several pose-specific, deep convolutional neural networks (CNN). 3D rendering is used to synthesize multiple face poses from input images to both train these models and to provide additional robustness to pose variations at test time. Our paper presents an extensive analysis of the IARPA Janus Benchmark A (IJB-A), evaluating the effects that landmark detection accuracy, CNN layer selection, and pose model selection all have on the performance of the recognition pipeline. It further provides comparative evaluations on IJB-A and the PIPA dataset. These tests show that our approach outperforms existing methods, even surprisingly matching the accuracy of methods that were specifically fine-tuned to the target dataset. Parts of this work previously appeared in [1] and [2].

שפה מקוריתאנגלית
מספר המאמר8255649
עמודים (מ-עד)379-393
מספר עמודים15
כתב עתIEEE Transactions on Pattern Analysis and Machine Intelligence
מספר גיליון2
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 1 פבר׳ 2019

הערה ביבליוגרפית

Publisher Copyright:
© 1979-2012 IEEE.

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