1,086 research outputs found
A landmark paper in face recognition
Good registration (alignment to a reference) is essential for accurate face recognition. The effects of the number of landmarks on the mean localization error and the recognition performance are studied. Two landmarking methods are explored and compared for that purpose: (1) the most likely-landmark locator (MLLL), based on maximizing the likelihood ratio, and (2) Viola-Jones detection. Both use the locations of facial features (eyes, nose, mouth, etc) as landmarks. Further, a landmark-correction method (BILBO) based on projection into a subspace is introduced. The MLLL has been trained for locating 17 landmarks and the Viola-Jones method for 5. The mean localization errors and effects on the verification performance have been measured. It was found that on the eyes, the Viola-Jones detector is about 1% of the interocular distance more accurate than the MLLL-BILBO combination. On the nose and mouth, the MLLL-BILBO combination is about 0.5% of the inter-ocular distance more accurate than the Viola-Jones detector. Using more landmarks will result in lower equal-error rates, even when the landmarking is not so accurate. If the same landmarks are used, the most accurate landmarking method gives the best verification performance
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
Unsupervised Performance Analysis of 3D Face Alignment
We address the problem of analyzing the performance of 3D face alignment
(3DFA) algorithms. Traditionally, performance analysis relies on carefully
annotated datasets. Here, these annotations correspond to the 3D coordinates of
a set of pre-defined facial landmarks. However, this annotation process, be it
manual or automatic, is rarely error-free, which strongly biases the analysis.
In contrast, we propose a fully unsupervised methodology based on robust
statistics and a parametric confidence test. We revisit the problem of robust
estimation of the rigid transformation between two point sets and we describe
two algorithms, one based on a mixture between a Gaussian and a uniform
distribution, and another one based on the generalized Student's
t-distribution. We show that these methods are robust to up to 50% outliers,
which makes them suitable for mapping a face, from an unknown pose to a frontal
pose, in the presence of facial expressions and occlusions. Using these methods
in conjunction with large datasets of face images, we build a statistical
frontal facial model and an associated parametric confidence metric, eventually
used for performance analysis. We empirically show that the proposed pipeline
is neither method-biased nor data-biased, and that it can be used to assess
both the performance of 3DFA algorithms and the accuracy of annotations of face
datasets
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