Deep Alignment Network: A Convolutional Neural Network for Robust Face Alignment
Marek Kowalski,Jacek Naruniec,Tomasz Trzcinski +2 more
- pp 2034-2043
Reads0
Chats0
TLDR
The use of entire face images rather than patches allows DAN to handle face images with large variation in head pose and difficult initializations, and reduces the state-of-the-art failure rate by up to 70%.Abstract:
In this paper, we propose Deep Alignment Network (DAN), a robust face alignment method based on a deep neural network architecture. DAN consists of multiple stages, where each stage improves the locations of the facial landmarks estimated by the previous stage. Our method uses entire face images at all stages, contrary to the recently proposed face alignment methods that rely on local patches. This is possible thanks to the use of landmark heatmaps which provide visual information about landmark locations estimated at the previous stages of the algorithm. The use of entire face images rather than patches allows DAN to handle face images with large variation in head pose and difficult initializations. An extensive evaluation on two publicly available datasets shows that DAN reduces the state-of-the-art failure rate by up to 70%. Our method has also been submitted for evaluation as part of the Menpo challenge.read more
Citations
More filters
Proceedings ArticleDOI
Robust Facial Landmark Detection via Occlusion-Adaptive Deep Networks
TL;DR: A simple and effective framework called Occlusion-adaptive Deep Networks (ODN) with the purpose of solving the occlusion problem for facial landmark detection and proposes a geometry-aware module to excavate geometric relationships between different facial components.
Book ChapterDOI
A Deeply-initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment
TL;DR: DCFE, a real-time facial landmark regression method based on a coarse-to-fine Ensemble of Regression Trees (ERT), uses a simple Convolutional Neural Network to generate probability maps of landmarks location and addresses the combinatorial explosion of parts deformation.
Proceedings ArticleDOI
AnonymousNet: Natural Face De-Identification With Measurable Privacy
TL;DR: Wang et al. as mentioned in this paper proposed a framework called AnonymousNet to balance usability and enhance privacy in a natural and measurable manner, which encompasses four stages: facial attribute estimation, privacy-metric-oriented face obfuscation, directed natural image synthesis, and adversarial perturbation.
Journal ArticleDOI
Real-Time Driver-Drowsiness Detection System Using Facial Features
Wanghua Deng,Ruoxue Wu +1 more
TL;DR: A system called DriCare is proposed, which detects the drivers’ fatigue status, such as yawning, blinking, and duration of eye closure, using video images, without equipping their bodies with devices, and can alert the driver using a fatigue warning.
Journal ArticleDOI
The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking
Jiankang Deng,Anastasios Roussos,Grigorios Chrysos,Evangelos Ververas,Irene Kotsia,Jie Shen,Stefanos Zafeiriou,Stefanos Zafeiriou +7 more
TL;DR: An elaborate semi-automatic methodology is introduced for providing high-quality annotations for both the Menpo 2D and Menpo 3D benchmarks, two new datasets for multi-pose 2d and 3D facial landmark localisation and tracking.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.