5,498 research outputs found
Random subspace method for aource camera identification
Sensor pattern noise is an inherent fingerprint of imaging devices, which has been widely used for source camera identification, image classification, and forgery detection. In a previous work, we proposed a feature extraction method based on the principal component analysis denoising concept, which can enhance the performance of conventional SPN extraction methods. However, this method is vulnerable, because the training samples are seriously affected by the image content. Accordingly, it is difficult to train a reliable feature extractor by using such a training set. To address this problem, a camera identification framework based on the random subspace method and majority voting is proposed in this work. The experimental results show that the proposed solution can suppress the interference from scene details and enhance the performance in terms of the receiver operating characteristic curve
Pooling Faces: Template based Face Recognition with Pooled Face Images
We propose a novel approach to template based face recognition. Our dual goal
is to both increase recognition accuracy and reduce the computational and
storage costs of template matching. To do this, we leverage on an approach
which was proven effective in many other domains, but, to our knowledge, never
fully explored for face images: average pooling of face photos. We show how
(and why!) the space of a template's images can be partitioned and then pooled
based on image quality and head pose and the effect this has on accuracy and
template size. We perform extensive tests on the IJB-A and Janus CS2 template
based face identification and verification benchmarks. These show that not only
does our approach outperform published state of the art despite requiring far
fewer cross template comparisons, but also, surprisingly, that image pooling
performs on par with deep feature pooling.Comment: Appeared in the IEEE Computer Society Workshop on Biometrics, IEEE
Conf. on Computer Vision and Pattern Recognition (CVPR), June, 201
Crossing Generative Adversarial Networks for Cross-View Person Re-identification
Person re-identification (\textit{re-id}) refers to matching pedestrians
across disjoint yet non-overlapping camera views. The most effective way to
match these pedestrians undertaking significant visual variations is to seek
reliably invariant features that can describe the person of interest
faithfully. Most of existing methods are presented in a supervised manner to
produce discriminative features by relying on labeled paired images in
correspondence. However, annotating pair-wise images is prohibitively expensive
in labors, and thus not practical in large-scale networked cameras. Moreover,
seeking comparable representations across camera views demands a flexible model
to address the complex distributions of images. In this work, we study the
co-occurrence statistic patterns between pairs of images, and propose to
crossing Generative Adversarial Network (Cross-GAN) for learning a joint
distribution for cross-image representations in a unsupervised manner. Given a
pair of person images, the proposed model consists of the variational
auto-encoder to encode the pair into respective latent variables, a proposed
cross-view alignment to reduce the view disparity, and an adversarial layer to
seek the joint distribution of latent representations. The learned latent
representations are well-aligned to reflect the co-occurrence patterns of
paired images. We empirically evaluate the proposed model against challenging
datasets, and our results show the importance of joint invariant features in
improving matching rates of person re-id with comparison to semi/unsupervised
state-of-the-arts.Comment: 12 pages. arXiv admin note: text overlap with arXiv:1702.03431 by
other author
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