Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 41, Issue 1, Pages 121-135Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2017.2781233
Keywords
Face detection; landmarks localization; head pose estimation; gender recognition; deep convolutional neural networks; multi-task learning
Funding
- Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) via IARPA RD [2014-14071600012]
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We present an algorithm for simultaneous face detection, landmarks localization, pose estimation and gender recognition using deep convolutional neural networks (CNN). The proposed method called, HyperFace, fuses the intermediate layers of a deep CNN using a separate CNN followed by a multi-task learning algorithm that operates on the fused features. It exploits the synergy among the tasks which boosts up their individual performances. Additionally, we propose two variants of HyperFace: (1) HyperFace-ResNet that builds on the ResNet-101 model and achieves significant improvement in performance, and (2) Fast-HyperFace that uses a high recall fast face detector for generating region proposals to improve the speed of the algorithm. Extensive experiments show that the proposed models are able to capture both global and local information in faces and performs significantly better than many competitive algorithms for each of these four tasks.
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