37 research outputs found
Template Adaptation for Face Verification and Identification
Face recognition performance evaluation has traditionally focused on
one-to-one verification, popularized by the Labeled Faces in the Wild dataset
for imagery and the YouTubeFaces dataset for videos. In contrast, the newly
released IJB-A face recognition dataset unifies evaluation of one-to-many face
identification with one-to-one face verification over templates, or sets of
imagery and videos for a subject. In this paper, we study the problem of
template adaptation, a form of transfer learning to the set of media in a
template. Extensive performance evaluations on IJB-A show a surprising result,
that perhaps the simplest method of template adaptation, combining deep
convolutional network features with template specific linear SVMs, outperforms
the state-of-the-art by a wide margin. We study the effects of template size,
negative set construction and classifier fusion on performance, then compare
template adaptation to convolutional networks with metric learning, 2D and 3D
alignment. Our unexpected conclusion is that these other methods, when combined
with template adaptation, all achieve nearly the same top performance on IJB-A
for template-based face verification and identification
VGGFace2: A dataset for recognising faces across pose and age
In this paper, we introduce a new large-scale face dataset named VGGFace2.
The dataset contains 3.31 million images of 9131 subjects, with an average of
362.6 images for each subject. Images are downloaded from Google Image Search
and have large variations in pose, age, illumination, ethnicity and profession
(e.g. actors, athletes, politicians). The dataset was collected with three
goals in mind: (i) to have both a large number of identities and also a large
number of images for each identity; (ii) to cover a large range of pose, age
and ethnicity; and (iii) to minimize the label noise. We describe how the
dataset was collected, in particular the automated and manual filtering stages
to ensure a high accuracy for the images of each identity. To assess face
recognition performance using the new dataset, we train ResNet-50 (with and
without Squeeze-and-Excitation blocks) Convolutional Neural Networks on
VGGFace2, on MS- Celeb-1M, and on their union, and show that training on
VGGFace2 leads to improved recognition performance over pose and age. Finally,
using the models trained on these datasets, we demonstrate state-of-the-art
performance on all the IARPA Janus face recognition benchmarks, e.g. IJB-A,
IJB-B and IJB-C, exceeding the previous state-of-the-art by a large margin.
Datasets and models are publicly available.Comment: This paper has been accepted by IEEE Conference on Automatic Face and
Gesture Recognition (F&G), 2018. (Oral
Face Identification and Clustering
In this thesis, we study two problems based on clustering algorithms. In the
first problem, we study the role of visual attributes using an agglomerative
clustering algorithm to whittle down the search area where the number of
classes is high to improve the performance of clustering. We observe that as we
add more attributes, the clustering performance increases overall. In the
second problem, we study the role of clustering in aggregating templates in a
1:N open set protocol using multi-shot video as a probe. We observe that by
increasing the number of clusters, the performance increases with respect to
the baseline and reaches a peak, after which increasing the number of clusters
causes the performance to degrade. Experiments are conducted using recently
introduced unconstrained IARPA Janus IJB-A, CS2, and CS3 face recognition
datasets