HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition
Open accessAbstract
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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Jointly modeling face detection with auxiliary tasks including face alignment, pose estimation, and gender classification can achieve improved performance owing to extra abundant supervisory signals for learning a shared discriminative feature representation.
“Recently, jointly modeling face detection with other auxiliary tasks including face alignment, pose estimation, and gender classification, can achieve improved performance, owing to the extra abundant supervisory signals for learning a shared discriminative feature representation [96], [97].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [97]
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- Authors
- Ranjan, R., Patel, V. M., & Chellappa, R.
- Affiliations
- University of Maryland, College Park (US)
- Rutgers, The State University of New Jersey (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2019-01-01
- Volume
- 41
- Issue
- 1
- Pages
- 121-135
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2290180618
- Cited by
- 1,333
- References
- 5
- Citations by year
- 2026: 34 · 2025: 53 · 2024: 84 · 2023: 126 · 2022: 145 · 2021: 192 · 2020: 230 · 2019: 248 · 2018: 132 · 2017: 77 · 2016: 11
- FWCI
- 77.98
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Office of the Director of National Intelligence
- Intelligence Advanced Research Projects Activity
- IARPA R&D Contract (2014-14071600012)
- ODNI
- IARPA
- U.S. Government
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- face detection, facial landmark detection, face pose estimation, gender classification, multi-task learning, feature fusion, ResNet-101, region proposal generation
- MeSH terms
- Deep Learning, Algorithms, Face, Female, Gender Identity, Humans, Image Processing, Computer-Assisted, Male, Pattern Recognition, Automated, Posture
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