713 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
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
Despite rapid advances in face recognition, there remains a clear gap between
the performance of still image-based face recognition and video-based face
recognition, due to the vast difference in visual quality between the domains
and the difficulty of curating diverse large-scale video datasets. This paper
addresses both of those challenges, through an image to video feature-level
domain adaptation approach, to learn discriminative video frame
representations. The framework utilizes large-scale unlabeled video data to
reduce the gap between different domains while transferring discriminative
knowledge from large-scale labeled still images. Given a face recognition
network that is pretrained in the image domain, the adaptation is achieved by
(i) distilling knowledge from the network to a video adaptation network through
feature matching, (ii) performing feature restoration through synthetic data
augmentation and (iii) learning a domain-invariant feature through a domain
adversarial discriminator. We further improve performance through a
discriminator-guided feature fusion that boosts high-quality frames while
eliminating those degraded by video domain-specific factors. Experiments on the
YouTube Faces and IJB-A datasets demonstrate that each module contributes to
our feature-level domain adaptation framework and substantially improves video
face recognition performance to achieve state-of-the-art accuracy. We
demonstrate qualitatively that the network learns to suppress diverse artifacts
in videos such as pose, illumination or occlusion without being explicitly
trained for them.Comment: accepted for publication at International Conference on Computer
Vision (ICCV) 201
A Proximity-Aware Hierarchical Clustering of Faces
In this paper, we propose an unsupervised face clustering algorithm called
"Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local
structure of deep representations. In the proposed method, a similarity measure
between deep features is computed by evaluating linear SVM margins. SVMs are
trained using nearest neighbors of sample data, and thus do not require any
external training data. Clusters are then formed by thresholding the similarity
scores. We evaluate the clustering performance using three challenging
unconstrained face datasets, including Celebrity in Frontal-Profile (CFP),
IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3)
datasets. Experimental results demonstrate that the proposed approach can
achieve significant improvements over state-of-the-art methods. Moreover, we
also show that the proposed clustering algorithm can be applied to curate a set
of large-scale and noisy training dataset while maintaining sufficient amount
of images and their variations due to nuisance factors. The face verification
performance on JANUS CS3 improves significantly by finetuning a DCNN model with
the curated MS-Celeb-1M dataset which contains over three million face images
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
DOMAIN ADAPTION FOR UNCONSTRAINED FACE VERIFICATION AND IDENTIFICATION
Face recognition has been receiving consistent attention in computer vision community for over three decades. Although recent advances in deep convolutional neural networks (DCNNs) have pushed face recognition algorithms to surpass human performance in most controlled situations, the unconstrained face recognition performance is still far from satisfactory. This is mainly because the domain shift between training and test data is substantial when faces are captured under extreme pose, blur or other covariates variations. In this dissertation, we study the effects of covariates and present approaches of mitigating the domain mismatch to improve the performance of unconstrained face verification and identification.
To study how covariates affect the performance of deep neural networks on the large-scale unconstrained face verification problem, we implement five state-of-the-art deep convolutional networks (DCNNs) and evaluate them on three challenging covariates datasets. In total, seven covariates are considered: pose (yaw and roll), age, facial hair, gender, indoor/outdoor, occlusion (nose and mouth visibility, and forehead visibility), and skin tone. Some of the results confirm and extend the findings of previous studies, while others are new findings that were rarely mentioned before or did not show consistent trends. In addition, we demonstrate that with the assistance of gender information, the quality of a pre-curated noisy large-scale face dataset can be further improved.
Based on the results of this study, we propose four domain adaptation methods to alleviate the effects of covariates. First, since we find that pose is a key factor for performance degradation, we propose a metric learning method to alleviate the effects of pose on face verification performance. We learn a joint model for face and pose verification tasks and explicitly discourage information sharing between the identity and pose metrics. Specifically, we enforce an orthogonal regularization constraint on the learned projection matrices for the two tasks leading to making the identity metrics for face verification more pose-robust. Extensive experiments are conducted on three challenging unconstrained face datasets that show promising results compared to state-of-the-art methods.
Second, to tackle the negative effects brought by image blur, we propose two approaches. The first approach is an incremental dictionary learning method to mitigate the distribution difference between sharp training data and blurred test data. Some blurred faces called supportive samples are selected, which are used for building more discriminative classification models and act as a bridge to connect the two domains. Second, we propose an unsupervised face deblurring approach based on disentangled representations. The disentanglement is achieved by splitting the content and blur features in a blurred image using content encoders and blur encoders. An adversarial loss is added on deblurred results to generate visually realistic faces. We conduct extensive experiments on two challenging face datasets that show promising results.
Finally, apart from the effects of pose and blur, face verification performance also suffers from the generic domain mismatch between source and target faces. To tackle this problem, we propose a template adaptation method for template-based face verification. A template-specific metric is trained to adaptively learn the discriminative information between test templates and the negative training set, which contains subjects that are mutually exclusive to subjects in test templates. Extensive experiments on two challenging face verification datasets yield promising results compared to other competitive methods
Bounded-Distortion Metric Learning
Metric learning aims to embed one metric space into another to benefit tasks
like classification and clustering. Although a greatly distorted metric space
has a high degree of freedom to fit training data, it is prone to overfitting
and numerical inaccuracy. This paper presents {\it bounded-distortion metric
learning} (BDML), a new metric learning framework which amounts to finding an
optimal Mahalanobis metric space with a bounded-distortion constraint. An
efficient solver based on the multiplicative weights update method is proposed.
Moreover, we generalize BDML to pseudo-metric learning and devise the
semidefinite relaxation and a randomized algorithm to approximately solve it.
We further provide theoretical analysis to show that distortion is a key
ingredient for stability and generalization ability of our BDML algorithm.
Extensive experiments on several benchmark datasets yield promising results
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