224,700 research outputs found
Cross-Domain Identification for Thermal-to-Visible Face Recognition
Recent advances in domain adaptation, especially those applied to
heterogeneous facial recognition, typically rely upon restrictive Euclidean
loss functions (e.g., norm) which perform best when images from two
different domains (e.g., visible and thermal) are co-registered and temporally
synchronized. This paper proposes a novel domain adaptation framework that
combines a new feature mapping sub-network with existing deep feature models,
which are based on modified network architectures (e.g., VGG16 or Resnet50).
This framework is optimized by introducing new cross-domain identity and domain
invariance loss functions for thermal-to-visible face recognition, which
alleviates the requirement for precisely co-registered and synchronized
imagery. We provide extensive analysis of both features and loss functions
used, and compare the proposed domain adaptation framework with
state-of-the-art feature based domain adaptation models on a difficult dataset
containing facial imagery collected at varying ranges, poses, and expressions.
Moreover, we analyze the viability of the proposed framework for more
challenging tasks, such as non-frontal thermal-to-visible face recognition
Cross-Domain Identification for Thermal-to-Visible Face Recognition
Recent advances in domain adaptation, especially those applied to heterogeneous facial recognition, typically rely upon restrictive Euclidean loss functions (e.g., L2 norm) which perform best when images from two different domains (e.g., visible and thermal) are co-registered and temporally synchronized. This paper proposes a novel domain adaptation framework that combines a new feature mapping sub-network with existing deep feature models, which are based on modified network architectures (e.g., VGG16 or Resnet50). This framework is optimized by introducing new cross-domain identity and domain invariance loss functions for thermal-to-visible face recognition, which alleviates the requirement for precisely co-registered and synchronized imagery. We provide extensive analysis of both features and loss functions used, and compare the proposed domain adaptation framework with state-of-the-art feature based domain adaptation models on a difficult dataset containing facial imagery collected at varying ranges, poses, and expressions. Moreover, we analyze the viability of the proposed framework for more challenging tasks, such as non-frontal thermal-to-visible face recognition
Multimodal emotion recognition via face and voice
Recent advances in technology have allowed humans to interact with computers in ways previously unimaginable. Despite significant progress, a necessary element for natural interaction is still lacking: emotions. Emotions play an important role in human communication and interaction, allowing people to express themselves beyond the language domain. The purpose of this project is to develop a multimodal system to classify emotions using facial expressions and the voice taken from videos. For face emotion recognition, face images and optical flow frames are used to exploit spatial and temporal information of the videos. Regarding the voice, the model uses speech features extracted from the chunked audio signals to predict the emotion. The combination of the two biometrics with a score-level fusion achieves excellent performance on the RAVDESS and the BAUM-1 datasets. However, the results remark the importance of further investigating the preprocessing techniques applied in this work to "normalize" the datasets to a unified format to improve the cross-dataset performance
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis
Polarimetric thermal to visible face verification entails matching two images
that contain significant domain differences. Several recent approaches have
attempted to synthesize visible faces from thermal images for cross-modal
matching. In this paper, we take a different approach in which rather than
focusing only on synthesizing visible faces from thermal faces, we also propose
to synthesize thermal faces from visible faces. Our intuition is based on the
fact that thermal images also contain some discriminative information about the
person for verification. Deep features from a pre-trained Convolutional Neural
Network (CNN) are extracted from the original as well as the synthesized
images. These features are then fused to generate a template which is then used
for verification. The proposed synthesis network is based on the self-attention
generative adversarial network (SAGAN) which essentially allows efficient
attention-guided image synthesis. Extensive experiments on the ARL polarimetric
thermal face dataset demonstrate that the proposed method achieves
state-of-the-art performance.Comment: This work is accepted at the 12th IAPR International Conference On
Biometrics (ICB 2019
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
While representation learning aims to derive interpretable features for
describing visual data, representation disentanglement further results in such
features so that particular image attributes can be identified and manipulated.
However, one cannot easily address this task without observing ground truth
annotation for the training data. To address this problem, we propose a novel
deep learning model of Cross-Domain Representation Disentangler (CDRD). By
observing fully annotated source-domain data and unlabeled target-domain data
of interest, our model bridges the information across data domains and
transfers the attribute information accordingly. Thus, cross-domain joint
feature disentanglement and adaptation can be jointly performed. In the
experiments, we provide qualitative results to verify our disentanglement
capability. Moreover, we further confirm that our model can be applied for
solving classification tasks of unsupervised domain adaptation, and performs
favorably against state-of-the-art image disentanglement and translation
methods.Comment: CVPR 2018 Spotligh
- …