Study uses stacked hourglass networks to improve facial landmark detection for medical diagnosis.
problem Improving accuracy of facial landmark detection for medical diagnosis.
method Conducted a study on landmark localisation methods using stacked hourglass networks.
result State-of-the-art stacked hourglass architecture outperforms traditional methods.
Paper evaluates CNN-based facial landmark detection methods.
problem Evaluate characteristics and performance of CNN-based facial landmark detection methods.
method Divided into regression and heatmap approaches, investigated using a hybrid loss function and discrimination network.
result Proposed model outperforms other models in all tested datasets.
Synthesizes faces from facial features, invariant to pose and expression.
problem Creating realistic face images from facial features.
method Learning facial landmarks and textures from facial-recognition features, training on frontal, neutral-expression images.
result Generated images are invariant to lighting, pose, and expression.
Study reveals biases in facial landmark detection methods for dementia patients.
problem Challenges in facial landmark detection for older adults with dementia.
method Evaluation of seven facial landmark detection methods on frontal, profile, and various face regions.
result Significant performance differences between dementia patients and non-patients, and biases across face regions.
Facial landmark localization and occlusion estimation for driver safety.
problem Robust facial landmark localization and occlusion estimation under harsh lighting and occlusion.
method Occluded Stacked Hourglass approach based on Stacked Hourglass network.
result State-of-the-art results in face detection, head pose, and occlusion estimation on various datasets.
Estimation of facial expressions, as spatio-temporal processes, can take advantage of kernel methods if one considers facial landmark positions and their motion in 3D space. We applied support vector classification with kernels derived from dynamic time-warping similarity measures. We achieved over 99% accuracy - measu…
Task-assisted domain adaptation improves performance on synthetic-to-real image tasks.
problem Models trained on synthetic images often fail to generalize to real images due to domain shift.
method Introduce anchor tasks with easy-to-obtain annotations, apply reeze technique to learn cross-task guidance.
result Using anchor tasks on both synthetic and real domains improves performance compared to training on one domain.
This work generates synthetic 3D thermal facial data using 2D facial data and deep learning.
problem Creating large datasets for deep learning in computer vision.
method 3D facial modelling techniques and deep learning methodologies.
result Synthetic 3D thermal facial data created for deep learning applications.
B-Spline CNNs on Lie Groups expand G-CNNs to arbitrary groups.
problem Leveraging geometric structure for improved feature learning.
method Lifting feature maps to B-spline expansions on Lie algebra.
result G-CNNs on Lie groups outperform classical 2D CNNs.
First steps towards a mathematical theory of deep convolutional neural networks for feature extraction were made---for the continuous-time case---in Mallat, 2012, and Wiatowski and Bölcskei, 2015. This paper considers the discrete case, introduces new convolutional neural network architectures, and proposes a mathemati…
New graph convolution captures local features on non-Euclidean grids.
problem Capturing local features on irregular, coarse non-Euclidean grids.
method Low-rank learnable local filters in graph convolutions.
result Proves more expressive than previous spectral graph convolution methods.
CAOS aggregates multiple one-shot predictors for efficient uncertainty quantification.
problem Lack of principled uncertainty quantification in one-shot prediction.
method CAOS, a conformal framework that aggregates multiple one-shot predictors and uses a leave-one-out calibration scheme.
result CAOS produces smaller prediction sets with reliable coverage compared to split conformal baselines.
The study extends stochastic completeness to landmark spaces with any number of landmarks.
problem Stochastic completeness for landmark spaces with arbitrary numbers of landmarks.
method Volume growth criterion and eigenvalue bounds for geodesic balls.
result Stochastic completeness for landmark spaces with any number of landmarks is proven.
Landmark2Vec maps unknown landmarks without GPS.
problem Estimate positions of unknown landmarks without GPS.
method Unsupervised neural network trained on landmark signals.
result Maps landmarks up to scale, rotation, and shift.
Method converts facial expressions and voice of a source speaker into a target speaker.
problem Separate conversion of facial and acoustic features leads to unnatural results.
method Uses three neural networks: conversion, waveform generation, and image reconstruction.
result Significantly higher naturalness achieved when converting both features together.
RRN accurately identifies landmarks in CMF bones without segmentation.
problem Accurately identifying landmarks in craniomaxillofacial bones without segmentation.
method End-to-end RRN architecture using dense-block units for landmark imputation.
result RRN achieves <2 mm RMS error in landmarking.
Unsupervised method discovers object landmarks by factorizing image deformations.
problem Learning object structure in unsupervised settings.
method Factorizing image deformations to learn landmarks consistently across different viewpoints and object deformations.
result Learned landmarks establish meaningful correspondences between different object instances without explicit requirement.
Proposes landmark selection for kernel methods.
problem Selecting important landmarks from large training sets.
method Deterministic and randomized adaptive algorithm for landmark selection.
result Landmarks are related to the minima of kernelized Christoffel functions.
Unified shape spaces with preserved invariances and regular metrics.
problem Combining shape invariances from Kendall's spaces with regular metrics.
method Defined a Sobolev-type operator to achieve the desired geometry, preserving invariances and regularity.
result Achieved a new landmark shape space with regular metrics and preserved invariances.
LA-VDM accelerates VDM using landmarks to improve data analysis.
problem Efficiently analyzing complex datasets with nonuniform sampling densities.
method Landmark-constrained two-stage normalization to accelerate VDM.
result LA-VDM accurately recovers parallel transport and converges to the connection Laplacian.
FEAFA dataset annotates facial expressions with high detail.
problem Lack of detailed facial expression annotations in existing datasets.
method Manual annotation of 122 participants' facial expressions.
result FEAFA dataset provides detailed annotations for facial expressions.
Landmark AD improves AD's efficiency without sacrificing performance.
problem Computational burden in diffusion-based sensor fusion.
method Inspired by landmark diffusion, proposes Landmark AD.
result Landmark AD offers superior computational efficiency.
Paper proposes a new landmark selection method for kernel ridge regression.
problem Efficient landmark selection for scalable kernel methods.
method Two-step approach: first computes importance scores, then clusters them into landmarks.
result Proposed method provides better accuracy and efficiency trade-offs.
Unsupervised model predicts facial attractiveness with high accuracy.
problem Capturing the complexity of facial attractiveness through machine learning.
method Infer probabilistic models of facial preferences using Maximum Entropy and neural networks.
result High prediction accuracy in gender classification of sculpting subjects.
Bayesian method learns optimal momentum for landmark matching.
problem Finding a diffeomorphism between two sets of landmarks.
method Ensemble Kalman filter for derivative-free Bayesian inverse method.
result Efficient algorithm for various target shapes.
NaimishNet detects facial key points using deep learning.
problem Facial key points detection in computer vision.
method Adapted LeNet deep CNN model for facial key points data.
result NaimishNet outperforms state-of-the-art approaches.
DeepLocalization uses neural networks to localize vehicles from landmarks.
problem Vehicle self-localization from multi-modal sensor data and a reference map.
method Deep neural network that regresses vehicle pose from unordered landmarks.
result DeepLocalization achieves state-of-the-art accuracy and is faster than related work.
Researchers prove long-time existence for two landmark Brownian motion.
problem Proving long-time existence of Brownian motion on configurations of two landmarks.
method Classification and analysis of long-time existence for configurations of exactly two landmarks, using a radial kernel.
result For configurations of exactly two landmarks, long-time existence is possible for certain kernels, but not for others.
Landmark-based node embeddings approximate shortest path distances in random graphs.
problem Capturing global graph distances in node representations.
method Landmark-based node embeddings using shortest path distances from a subset of reference nodes (landmarks).
result Random graphs require lower dimensions in landmark-based embeddings compared to worst-case graphs.
Exact universal interpolation property for landmark configurations in Euclidean space.
problem Representing and deforming landmark configurations through flows of vector fields.
method Explicitly describe vector fields for exact universal interpolation property in all dimensions.
result Achieve controllability by combining constant and polynomial vector fields.
Deep learning model detects landmarks in X-ray cephalograms.
problem Automatically detecting landmarks in cephalometric X-ray images.
method 2-stage U-Net with attention mechanism and Expansive Exploration strategy.
result State-of-the-art results in landmark detection.
Fawkes protects images from unauthorized facial recognition models.
problem Unauthorized training of facial recognition models poses privacy risks.
method Fawkes adds imperceptible pixel-level changes (cloaks) to images before release.
result Fawkes can protect images from misidentification by 95% and 80% even when clean images are leaked.
Efficient spectral clustering using autoencoders and landmarks.
problem High computational complexity of spectral clustering.
method Build adjacency matrix using landmarks, define efficient Laplacian matrix, perform eigen decomposition using autoencoder.
result Overall complexity of O(np), where n is data points and p is landmarks. Neumann eigenmaps improve landmark-based diffusion map embeddings.
problem Landmark-based diffusion map embeddings can be computationally inefficient and unstable.
method NeuMaps use a renormalized Neumann Laplacian for eigendecomposition, incorporating landmarks as a subgraph.
result NeuMaps offer a computationally efficient and stable embedding method.
The paper uses facial keypoints to estimate post-surgical pain intensity.
problem Accurately assessing pain levels from self-reported ratings is challenging.
method The approach analyzes 2D and 3D facial keypoints to estimate pain intensity.
result The pain estimation model uses multiple instance learning.
Paper introduces conformal prediction for reliable uncertainty quantification in landmark localization.
problem Systematic underestimation of total predictive uncertainty in landmark localization.
method Conformal prediction framework for multi-output regression, generating flexible prediction regions.
result Methods outperform existing approaches in validity and efficiency across 2D and 3D datasets.
New landmark states improve transfer learning in multi-task RL.
problem Improving sample complexity and regret in new RL tasks.
method Topological landmark covering, landmark value functions, action pruning.
result Theoretical bounds on Q values at state-action pairs.
Survey examines public views on facial recognition technology.
problem Public acceptance and privacy concerns with facial recognition.
method Cross-national survey of 4 countries.
result Public views vary significantly across countries.
Novel deep learning framework for mandible segmentation and landmarking.
problem Challenging problem of mandible segmentation and anatomical landmarking from CBCT scans.
method Three-step approach: deep neural network for segmentation, geodesic space for landmarks, LSTM for closely spaced landmarks.
result Superior efficacy compared to state-of-the-art methods in craniofacial anomalies and diseased states.
Polynomial fusion layer improves speech-driven facial animation.
problem Recent facial synthesis relies on low-dimensional representations and concatenation, ignoring higher-order interactions.
method Proposes a polynomial fusion layer to model higher-order interactions of facial encodings.
result Demonstrates improved video quality, audiovisual synchronisation, and blink generation.
The paper explores a new method for landmark matching using sub-Riemannian geometry and neural networks.
problem Finding a time-dependent vector field to warp points from an initial set to a target set.
method Sub-Riemannian geometry and residual neural networks.
result Demonstrates the importance of regularization in landmark matching.
This review explores automated pain detection from facial expressions using FACS.
problem Automated detection of pain from facial expressions is challenging and underexplored.
method Examines progress in AFER and APD, focusing on FACS-based methods and deep learning.
result Limited studies on APD, highlighting the need for clinical applications.
Characterizes geodesic completeness for landmark spaces.
problem Ensuring geodesics exist for all times in landmark spaces.
method Integrability criterion based on cometric kernel behavior.
result Full characterization of geodesic completeness for smooth Riemannian metrics.
Kernel methods obtain superb performance in terms of accuracy for various machine learning tasks since they can effectively extract nonlinear relations. However, their time complexity can be rather large especially for clustering tasks. In this paper we define a general class of kernels that can be easily approximated …
Unified facial behavior analysis network improves performance across tasks.
problem Independent study of facial behavior tasks.
method Single multi-task, multi-domain, multi-label network (FaceBehaviorNet).
result Joint training of facial behavior tasks yields better performance.
New method transfers emotions in facial images.
problem Transforming facial images to different emotions.
method Infinite task learning and vector-valued reproducing kernel Hilbert spaces.
result Achieves low reconstruction cost and high emotion classification accuracy.
This work predicts and interpolates long-range videos using unsupervised landmarks.
problem Predicting and interpolating long-range video data with occlusions and appearance changes.
method Unsupervised latent structure inference followed by temporal prediction in a latent space.
result High-quality long-range video interpolation and extrapolation achieved through landmark representation.
Proposes a linear model for facial action recognition without requiring large datasets.
problem Limited annotated data for facial expression and action units.
method Exploits low-rank property across frames and group sparsity to subtract neutral faces and recognize actions.
result One-shot automatic method on raw face videos performs competitively and better than previous methods.