12 research outputs found
Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion
Facial landmark detection, head pose estimation, and facial deformation
analysis are typical facial behavior analysis tasks in computer vision. The
existing methods usually perform each task independently and sequentially,
ignoring their interactions. To tackle this problem, we propose a unified
framework for simultaneous facial landmark detection, head pose estimation, and
facial deformation analysis, and the proposed model is robust to facial
occlusion. Following a cascade procedure augmented with model-based head pose
estimation, we iteratively update the facial landmark locations, facial
occlusion, head pose and facial de- formation until convergence. The
experimental results on benchmark databases demonstrate the effectiveness of
the proposed method for simultaneous facial landmark detection, head pose and
facial deformation estimation, even if the images are under facial occlusion.Comment: International Conference on Computer Vision and Pattern Recognition,
201
Registration-free Face-SSD: Single shot analysis of smiles, facial attributes, and affect in the wild
In this paper, we present a novel single shot face-related task analysis
method, called Face-SSD, for detecting faces and for performing various
face-related (classification/regression) tasks including smile recognition,
face attribute prediction and valence-arousal estimation in the wild. Face-SSD
uses a Fully Convolutional Neural Network (FCNN) to detect multiple faces of
different sizes and recognise/regress one or more face-related classes.
Face-SSD has two parallel branches that share the same low-level filters, one
branch dealing with face detection and the other one with face analysis tasks.
The outputs of both branches are spatially aligned heatmaps that are produced
in parallel - therefore Face-SSD does not require that face detection, facial
region extraction, size normalisation, and facial region processing are
performed in subsequent steps. Our contributions are threefold: 1) Face-SSD is
the first network to perform face analysis without relying on pre-processing
such as face detection and registration in advance - Face-SSD is a simple and a
single FCNN architecture simultaneously performing face detection and
face-related task analysis - those are conventionally treated as separate
consecutive tasks; 2) Face-SSD is a generalised architecture that is applicable
for various face analysis tasks without modifying the network structure - this
is in contrast to designing task-specific architectures; and 3) Face-SSD
achieves real-time performance (21 FPS) even when detecting multiple faces and
recognising multiple classes in a given image. Experimental results show that
Face-SSD achieves state-of-the-art performance in various face analysis tasks
by reaching a recognition accuracy of 95.76% for smile detection, 90.29% for
attribute prediction, and Root Mean Square (RMS) error of 0.44 and 0.39 for
valence and arousal estimation
Deep Learning for Head Pose Estimation: A Survey
Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the feld by proposing a classifcation of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the diferent ways deep learning techniques have been used in the context of HPE. An
in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic