8,175 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
A Discriminatively Learned CNN Embedding for Person Re-identification
We revisit two popular convolutional neural networks (CNN) in person
re-identification (re-ID), i.e, verification and classification models. The two
models have their respective advantages and limitations due to different loss
functions. In this paper, we shed light on how to combine the two models to
learn more discriminative pedestrian descriptors. Specifically, we propose a
new siamese network that simultaneously computes identification loss and
verification loss. Given a pair of training images, the network predicts the
identities of the two images and whether they belong to the same identity. Our
network learns a discriminative embedding and a similarity measurement at the
same time, thus making full usage of the annotations. Albeit simple, the
learned embedding improves the state-of-the-art performance on two public
person re-ID benchmarks. Further, we show our architecture can also be applied
in image retrieval
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