617 research outputs found
Deep Sketch-Photo Face Recognition Assisted by Facial Attributes
In this paper, we present a deep coupled framework to address the problem of
matching sketch image against a gallery of mugshots. Face sketches have the
essential in- formation about the spatial topology and geometric details of
faces while missing some important facial attributes such as ethnicity, hair,
eye, and skin color. We propose a cou- pled deep neural network architecture
which utilizes facial attributes in order to improve the sketch-photo
recognition performance. The proposed Attribute-Assisted Deep Con- volutional
Neural Network (AADCNN) method exploits the facial attributes and leverages the
loss functions from the facial attributes identification and face verification
tasks in order to learn rich discriminative features in a common em- bedding
subspace. The facial attribute identification task increases the inter-personal
variations by pushing apart the embedded features extracted from individuals
with differ- ent facial attributes, while the verification task reduces the
intra-personal variations by pulling together all the fea- tures that are
related to one person. The learned discrim- inative features can be well
generalized to new identities not seen in the training data. The proposed
architecture is able to make full use of the sketch and complementary fa- cial
attribute information to train a deep model compared to the conventional
sketch-photo recognition methods. Exten- sive experiments are performed on
composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The
results show the superiority of our method compared to the state- of-the-art
models in sketch-photo recognition algorithm
Attention for Robot Touch: Tactile Saliency Prediction for Robust Sim-to-Real Tactile Control
High-resolution tactile sensing can provide accurate information about local
contact in contact-rich robotic tasks. However, the deployment of such tasks in
unstructured environments remains under-investigated. To improve the robustness
of tactile robot control in unstructured environments, we propose and study a
new concept: \textit{tactile saliency} for robot touch, inspired by the human
touch attention mechanism from neuroscience and the visual saliency prediction
problem from computer vision. In analogy to visual saliency, this concept
involves identifying key information in tactile images captured by a tactile
sensor. While visual saliency datasets are commonly annotated by humans,
manually labelling tactile images is challenging due to their counterintuitive
patterns. To address this challenge, we propose a novel approach comprised of
three interrelated networks: 1) a Contact Depth Network (ConDepNet), which
generates a contact depth map to localize deformation in a real tactile image
that contains target and noise features; 2) a Tactile Saliency Network
(TacSalNet), which predicts a tactile saliency map to describe the target areas
for an input contact depth map; 3) and a Tactile Noise Generator (TacNGen),
which generates noise features to train the TacSalNet. Experimental results in
contact pose estimation and edge-following in the presence of distractors
showcase the accurate prediction of target features from real tactile images.
Overall, our tactile saliency prediction approach gives robust sim-to-real
tactile control in environments with unknown distractors. Project page:
https://sites.google.com/view/tactile-saliency/.Comment: Accepted by IROS 202
Proceedings of the Graduate Student Symposium of the 7th International Conference on the Theory and Application of Diagrams, July 5 2012
Proceedings of the Graduate Student Symposium held at the 7th International Conference on the Theory and Application of Diagrams, ( Diagrams 2012 ), held at the University of Kent on July 5, 2012. Dr. Nathaniel Miller, professor of in the School of Mathematical Sciences at UNC, served on the symposium organizing committee
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