1,711 research outputs found
Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos
In recent years, heatmap regression based models have shown their
effectiveness in face alignment and pose estimation. However, Conventional
Heatmap Regression (CHR) is not accurate nor stable when dealing with
high-resolution facial videos, since it finds the maximum activated location in
heatmaps which are generated from rounding coordinates, and thus leads to
quantization errors when scaling back to the original high-resolution space. In
this paper, we propose a Fractional Heatmap Regression (FHR) for
high-resolution video-based face alignment. The proposed FHR can accurately
estimate the fractional part according to the 2D Gaussian function by sampling
three points in heatmaps. To further stabilize the landmarks among continuous
video frames while maintaining the precise at the same time, we propose a novel
stabilization loss that contains two terms to address time delay and non-smooth
issues, respectively. Experiments on 300W, 300-VW and Talking Face datasets
clearly demonstrate that the proposed method is more accurate and stable than
the state-of-the-art models.Comment: Accepted to AAAI 2019. 8 pages, 7 figure
Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings
Conventional feature-based and model-based gaze estimation methods have
proven to perform well in settings with controlled illumination and specialized
cameras. In unconstrained real-world settings, however, such methods are
surpassed by recent appearance-based methods due to difficulties in modeling
factors such as illumination changes and other visual artifacts. We present a
novel learning-based method for eye region landmark localization that enables
conventional methods to be competitive to latest appearance-based methods.
Despite having been trained exclusively on synthetic data, our method exceeds
the state of the art for iris localization and eye shape registration on
real-world imagery. We then use the detected landmarks as input to iterative
model-fitting and lightweight learning-based gaze estimation methods. Our
approach outperforms existing model-fitting and appearance-based methods in the
context of person-independent and personalized gaze estimation
Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
Head-pose estimation has many applications, such as social event analysis,
human-robot and human-computer interaction, driving assistance, and so forth.
Head-pose estimation is challenging because it must cope with changing
illumination conditions, variabilities in face orientation and in appearance,
partial occlusions of facial landmarks, as well as bounding-box-to-face
alignment errors. We propose tu use a mixture of linear regressions with
partially-latent output. This regression method learns to map high-dimensional
feature vectors (extracted from bounding boxes of faces) onto the joint space
of head-pose angles and bounding-box shifts, such that they are robustly
predicted in the presence of unobservable phenomena. We describe in detail the
mapping method that combines the merits of unsupervised manifold learning
techniques and of mixtures of regressions. We validate our method with three
publicly available datasets and we thoroughly benchmark four variants of the
proposed algorithm with several state-of-the-art head-pose estimation methods.Comment: 12 pages, 5 figures, 3 table
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