26,712 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
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
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
One-to-many face recognition with bilinear CNNs
The recent explosive growth in convolutional neural network (CNN) research
has produced a variety of new architectures for deep learning. One intriguing
new architecture is the bilinear CNN (B-CNN), which has shown dramatic
performance gains on certain fine-grained recognition problems [15]. We apply
this new CNN to the challenging new face recognition benchmark, the IARPA Janus
Benchmark A (IJB-A) [12]. It features faces from a large number of identities
in challenging real-world conditions. Because the face images were not
identified automatically using a computerized face detection system, it does
not have the bias inherent in such a database. We demonstrate the performance
of the B-CNN model beginning from an AlexNet-style network pre-trained on
ImageNet. We then show results for fine-tuning using a moderate-sized and
public external database, FaceScrub [17]. We also present results with
additional fine-tuning on the limited training data provided by the protocol.
In each case, the fine-tuned bilinear model shows substantial improvements over
the standard CNN. Finally, we demonstrate how a standard CNN pre-trained on a
large face database, the recently released VGG-Face model [20], can be
converted into a B-CNN without any additional feature training. This B-CNN
improves upon the CNN performance on the IJB-A benchmark, achieving 89.5%
rank-1 recall.Comment: Published version at WACV 201
GhostVLAD for set-based face recognition
The objective of this paper is to learn a compact representation of image
sets for template-based face recognition. We make the following contributions:
first, we propose a network architecture which aggregates and embeds the face
descriptors produced by deep convolutional neural networks into a compact
fixed-length representation. This compact representation requires minimal
memory storage and enables efficient similarity computation. Second, we propose
a novel GhostVLAD layer that includes {\em ghost clusters}, that do not
contribute to the aggregation. We show that a quality weighting on the input
faces emerges automatically such that informative images contribute more than
those with low quality, and that the ghost clusters enhance the network's
ability to deal with poor quality images. Third, we explore how input feature
dimension, number of clusters and different training techniques affect the
recognition performance. Given this analysis, we train a network that far
exceeds the state-of-the-art on the IJB-B face recognition dataset. This is
currently one of the most challenging public benchmarks, and we surpass the
state-of-the-art on both the identification and verification protocols.Comment: Accepted by ACCV 201
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