An All-In-One Convolutional Neural Network for Face Analysis
About
We present a multi-purpose algorithm for simultaneous face detection, face alignment, pose estimation, gender recognition, smile detection, age estimation and face recognition using a single deep convolutional neural network (CNN). The proposed method employs a multi-task learning framework that regularizes the shared parameters of CNN and builds a synergy among different domains and tasks. Extensive experiments show that the network has a better understanding of face and achieves state-of-the-art result for most of these tasks.
Rajeev Ranjan, Swami Sankaranarayanan, Carlos D. Castillo, Rama Chellappa• 2016
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Facial Attribute Classification | CelebA | -- | 163 | |
| Face Search | IJB-A | Rank@194.7 | 44 | |
| Face Verification | IJB-A | TAR @ FAR=1%0.922 | 38 | |
| Face Verification | IJB-A (test) | TAR @ FAR=0.010.922 | 37 | |
| Face Identification | IJB-A (test) | Rank-194.7 | 30 | |
| Age Estimation | Chalearn LAP 2015 (val) | Error0.293 | 25 | |
| Age Estimation | FG-NET (LOPO) | Average Error2 | 17 | |
| Face Recognition | IJB-A (test) | TAR @ FAR=0.0192.2 | 16 | |
| Face Alignment | AFLW | -- | 12 | |
| Landmarks Localization | AFLW subset 21 pts (test) | NME Bin [0, 30]2.84 | 7 |
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