21,845 research outputs found
A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking
Person re identification is a challenging retrieval task that requires
matching a person's acquired image across non overlapping camera views. In this
paper we propose an effective approach that incorporates both the fine and
coarse pose information of the person to learn a discriminative embedding. In
contrast to the recent direction of explicitly modeling body parts or
correcting for misalignment based on these, we show that a rather
straightforward inclusion of acquired camera view and/or the detected joint
locations into a convolutional neural network helps to learn a very effective
representation. To increase retrieval performance, re-ranking techniques based
on computed distances have recently gained much attention. We propose a new
unsupervised and automatic re-ranking framework that achieves state-of-the-art
re-ranking performance. We show that in contrast to the current
state-of-the-art re-ranking methods our approach does not require to compute
new rank lists for each image pair (e.g., based on reciprocal neighbors) and
performs well by using simple direct rank list based comparison or even by just
using the already computed euclidean distances between the images. We show that
both our learned representation and our re-ranking method achieve
state-of-the-art performance on a number of challenging surveillance image and
video datasets.
The code is available online at:
https://github.com/pse-ecn/pose-sensitive-embeddingComment: CVPR 2018: v2 (fixes, added new results on PRW dataset
Pigment Melanin: Pattern for Iris Recognition
Recognition of iris based on Visible Light (VL) imaging is a difficult
problem because of the light reflection from the cornea. Nonetheless, pigment
melanin provides a rich feature source in VL, unavailable in Near-Infrared
(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical
not stimulated in NIR. In this case, a plausible solution to observe such
patterns may be provided by an adaptive procedure using a variational technique
on the image histogram. To describe the patterns, a shape analysis method is
used to derive feature-code for each subject. An important question is how much
the melanin patterns, extracted from VL, are independent of iris texture in
NIR. With this question in mind, the present investigation proposes fusion of
features extracted from NIR and VL to boost the recognition performance. We
have collected our own database (UTIRIS) consisting of both NIR and VL images
of 158 eyes of 79 individuals. This investigation demonstrates that the
proposed algorithm is highly sensitive to the patterns of cromophores and
improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on
Instruments and Measurements, Volume 59, Issue number 4, April 201
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