4,960 research outputs found
Robust multi-clue face tracking system
In this paper we present a multi-clue face tracking system, based on the combination of a face detector and two independent trackers. The detector, a variant of the Viola-Jones algorithm, is set to generate very low false positive error rate. It initiates the tracking system and updates its state. The trackers, based on 3DRS and optical flow respectively, have been chosen to complement each other in different conditions. The main focus of this work is the integration of the two trackers and the design of a closed loop detector-tracker system, aiming at achieving superior robustness at real-time operation on a PC platform. Tests were carried out to assess the actual performance of the system. With an average of about 95% correct face location rate and no significant false positives, the proposed approach appears to be particularly robust to complex backgrounds, ambient light variation, face orientation and scale changes, partial occlusions, different\ud
facial expressions and presence of other unwanted faces
MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild
Face tracking serves as the crucial initial step in mobile applications
trying to analyse target faces over time in mobile settings. However, this
problem has received little attention, mainly due to the scarcity of dedicated
face tracking benchmarks. In this work, we introduce MobiFace, the first
dataset for single face tracking in mobile situations. It consists of 80
unedited live-streaming mobile videos captured by 70 different smartphone users
in fully unconstrained environments. Over bounding boxes are manually
labelled. The videos are carefully selected to cover typical smartphone usage.
The videos are also annotated with 14 attributes, including 6 newly proposed
attributes and 8 commonly seen in object tracking. 36 state-of-the-art
trackers, including facial landmark trackers, generic object trackers and
trackers that we have fine-tuned or improved, are evaluated. The results
suggest that mobile face tracking cannot be solved through existing approaches.
In addition, we show that fine-tuning on the MobiFace training data
significantly boosts the performance of deep learning-based trackers,
suggesting that MobiFace captures the unique characteristics of mobile face
tracking. Our goal is to offer the community a diverse dataset to enable the
design and evaluation of mobile face trackers. The dataset, annotations and the
evaluation server will be on \url{https://mobiface.github.io/}.Comment: To appear on The 14th IEEE International Conference on Automatic Face
and Gesture Recognition (FG 2019
Efficient illumination independent appearance-based face tracking
One of the major challenges that visual tracking algorithms face nowadays is being
able to cope with changes in the appearance of the target during tracking. Linear
subspace models have been extensively studied and are possibly the most popular
way of modelling target appearance. We introduce a linear subspace representation
in which the appearance of a face is represented by the addition of two approxi-
mately independent linear subspaces modelling facial expressions and illumination
respectively. This model is more compact than previous bilinear or multilinear ap-
proaches. The independence assumption notably simplifies system training. We only
require two image sequences. One facial expression is subject to all possible illumina-
tions in one sequence and the face adopts all facial expressions under one particular
illumination in the other. This simple model enables us to train the system with
no manual intervention. We also revisit the problem of efficiently fitting a linear
subspace-based model to a target image and introduce an additive procedure for
solving this problem. We prove that Matthews and Bakerâs Inverse Compositional
Approach makes a smoothness assumption on the subspace basis that is equiva-
lent to Hager and Belhumeurâs, which worsens convergence. Our approach differs
from Hager and Belhumeurâs additive and Matthews and Bakerâs compositional ap-
proaches in that we make no smoothness assumptions on the subspace basis. In the
experiments conducted we show that the model introduced accurately represents
the appearance variations caused by illumination changes and facial expressions.
We also verify experimentally that our fitting procedure is more accurate and has
better convergence rate than the other related approaches, albeit at the expense of
a slight increase in computational cost. Our approach can be used for tracking a
human face at standard video frame rates on an average personal computer
Face Tracking dan Distance Estimation pada Realtime Video Menggunakan 3d Stereo Vision Camera
Face Tracking dan Distance Estimation pada realtime video menggunakan 3D stereo vision camera yang diajukan dalam paper ini adalah sebuah sistem deteksi wajah dan pengukuran estimasi jarak obyek wajah yang terdeteksi menggunakan 3D stereo vision camera. Dalam penelitian ini dikembangkan sistem untuk deteksi wajah menggunakan Haar Cascade Classifier dan untuk pengukuran estimasi jarak wajah dengan kamera menggunakan proyeksi gambar 2D menjadi 3D. Data 3 dimensi pada stereo vision kamera yang digunakan dapat direkonstruksi menggunakan proyeksi 2 Dimensi dari 2 buah titik kamera pada stereo vision camera. Implementasi deteksi wajah (face tracking) dan estimasi jarak pada realtime video menggunakan stereo vision camera yang diusulkan dapat bekerja untuk mendeteksi setiap obyek wajah manusia dengan baik, dan mampu memberikan estimasi jarak antara obyek wajah yang ditangkap dengan stereo vision camera secara riil. Deteksi wajah dan estimasi jarak wajah yang optimal adalah pada kisaran jarak 51-200cm, dengan deteksi wajah dan estimasi jarak yang ideal adalah pada posisi frontal view. Dari percobaan yang dilakukan dapat dihasilkan sebuah sistem tracking wajah yang robust dan dapat diketahui akurasi perhitungan estimasi jarak dibandingkan dengan jarak riil wajah mencapai 94.74 %
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second
authorshi
Face tracking using a hyperbolic catadioptric omnidirectional system
In the first part of this paper, we present a brief review on catadioptric omnidirectional
systems. The special case of the hyperbolic omnidirectional system is analysed in depth.
The literature shows that a hyperboloidal mirror has two clear advantages over alternative
geometries. Firstly, a hyperboloidal mirror has a single projection centre [1]. Secondly, the
image resolution is uniformly distributed along the mirrorâs radius [2].
In the second part of this paper we show empirical results for the detection and tracking
of faces from the omnidirectional images using Viola-Jones method. Both panoramic and
perspective projections, extracted from the omnidirectional image, were used for that purpose.
The omnidirectional image size was 480x480 pixels, in greyscale. The tracking method used
regions of interest (ROIs) set as the result of the detections of faces from a panoramic projection
of the image. In order to avoid losing or duplicating detections, the panoramic projection was
extended horizontally. Duplications were eliminated based on the ROIs established by previous
detections. After a confirmed detection, faces were tracked from perspective projections (which
are called virtual cameras), each one associated with a particular face. The zoom, pan and tilt
of each virtual camera was determined by the ROIs previously computed on the panoramic
image.
The results show that, when using a careful combination of the two projections, good frame
rates can be achieved in the task of tracking faces reliably
3D Face Tracking and Texture Fusion in the Wild
We present a fully automatic approach to real-time 3D face reconstruction
from monocular in-the-wild videos. With the use of a cascaded-regressor based
face tracking and a 3D Morphable Face Model shape fitting, we obtain a
semi-dense 3D face shape. We further use the texture information from multiple
frames to build a holistic 3D face representation from the video frames. Our
system is able to capture facial expressions and does not require any
person-specific training. We demonstrate the robustness of our approach on the
challenging 300 Videos in the Wild (300-VW) dataset. Our real-time fitting
framework is available as an open source library at http://4dface.org
3D Face tracking and gaze estimation using a monocular camera
Estimating a userâs gaze direction, one of the main novel user interaction technologies, will eventually be used for numerous applications where current methods are becoming less effective. In this paper, a new method is presented for estimating the gaze direction using Canonical Correlation Analysis (CCA), which ïŹnds a linear relationship between two datasets deïŹning the face pose and the corresponding facial appearance changes. Afterwards, iris tracking is performed by blob detection using a 4-connected component labeling algorithm. Finally, a gaze vector is calculated based on gathered eye properties. Results obtained from datasets and real-time input conïŹrm the robustness of this metho
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