199,464 research outputs found
Infrared face recognition: a comprehensive review of methodologies and databases
Automatic face recognition is an area with immense practical potential which
includes a wide range of commercial and law enforcement applications. Hence it
is unsurprising that it continues to be one of the most active research areas
of computer vision. Even after over three decades of intense research, the
state-of-the-art in face recognition continues to improve, benefitting from
advances in a range of different research fields such as image processing,
pattern recognition, computer graphics, and physiology. Systems based on
visible spectrum images, the most researched face recognition modality, have
reached a significant level of maturity with some practical success. However,
they continue to face challenges in the presence of illumination, pose and
expression changes, as well as facial disguises, all of which can significantly
decrease recognition accuracy. Amongst various approaches which have been
proposed in an attempt to overcome these limitations, the use of infrared (IR)
imaging has emerged as a particularly promising research direction. This paper
presents a comprehensive and timely review of the literature on this subject.
Our key contributions are: (i) a summary of the inherent properties of infrared
imaging which makes this modality promising in the context of face recognition,
(ii) a systematic review of the most influential approaches, with a focus on
emerging common trends as well as key differences between alternative
methodologies, (iii) a description of the main databases of infrared facial
images available to the researcher, and lastly (iv) a discussion of the most
promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap
with arXiv:1306.160
End-to-End Localization and Ranking for Relative Attributes
We propose an end-to-end deep convolutional network to simultaneously
localize and rank relative visual attributes, given only weakly-supervised
pairwise image comparisons. Unlike previous methods, our network jointly learns
the attribute's features, localization, and ranker. The localization module of
our network discovers the most informative image region for the attribute,
which is then used by the ranking module to learn a ranking model of the
attribute. Our end-to-end framework also significantly speeds up processing and
is much faster than previous methods. We show state-of-the-art ranking results
on various relative attribute datasets, and our qualitative localization
results clearly demonstrate our network's ability to learn meaningful image
patches.Comment: Appears in European Conference on Computer Vision (ECCV), 201
Data-Driven Segmentation of Post-mortem Iris Images
This paper presents a method for segmenting iris images obtained from the
deceased subjects, by training a deep convolutional neural network (DCNN)
designed for the purpose of semantic segmentation. Post-mortem iris recognition
has recently emerged as an alternative, or additional, method useful in
forensic analysis. At the same time it poses many new challenges from the
technological standpoint, one of them being the image segmentation stage, which
has proven difficult to be reliably executed by conventional iris recognition
methods. Our approach is based on the SegNet architecture, fine-tuned with
1,300 manually segmented post-mortem iris images taken from the
Warsaw-BioBase-Post-Mortem-Iris v1.0 database. The experiments presented in
this paper show that this data-driven solution is able to learn specific
deformations present in post-mortem samples, which are missing from alive
irises, and offers a considerable improvement over the state-of-the-art,
conventional segmentation algorithm (OSIRIS): the Intersection over Union (IoU)
metric was improved from 73.6% (for OSIRIS) to 83% (for DCNN-based presented in
this paper) averaged over subject-disjoint, multiple splits of the data into
train and test subsets. This paper offers the first known to us method of
automatic processing of post-mortem iris images. We offer source codes with the
trained DCNN that perform end-to-end segmentation of post-mortem iris images,
as described in this paper. Also, we offer binary masks corresponding to manual
segmentation of samples from Warsaw-BioBase-Post-Mortem-Iris v1.0 database to
facilitate development of alternative methods for post-mortem iris
segmentation
Fearful faces have a sensory advantage in the competition for awareness
Only a subset of visual signals give rise to a conscious percept. Threat signals, such as fearful faces, are particularly salient to human vision. Research suggests that fearful faces are evaluated without awareness and preferentially promoted to conscious perception. This agrees with evolutionary theories that posit a dedicated pathway specialized in processing threat-relevant signals. We propose an alternative explanation for this "fear advantage." Using psychophysical data from continuous flash suppression (CFS) and masking experiments, we demonstrate that awareness of facial expressions is predicted by effective contrast: the relationship between their Fourier spectrum and the contrast sensitivity function. Fearful faces have higher effective contrast than neutral expressions and this, not threat content, predicts their enhanced access to awareness. Importantly, our findings do not support the existence of a specialized mechanism that promotes threatening stimuli to awareness. Rather, our data suggest that evolutionary or learned adaptations have molded the fearful expression to exploit our general-purpose sensory mechanisms
Call Me Caitlyn: Making and making over the 'authentic' transgender body in Anglo-American popular culture
A conception of transgender identity as an ‘authentic’ gendered core ‘trapped’ within a mismatched corporeality, and made tangible through corporeal transformations, has attained unprecedented legibility in contemporary Anglo-American media. Whilst pop-cultural articulations of this discourse have received some scholarly attention, the question of why this 'wrong body' paradigm has solidified as the normative explanation for gender transition within the popular media remains underexplored. This paper argues that this discourse has attained cultural pre-eminence through its convergence with a broader media and commercial zeitgeist, in which corporeal alteration and maintenance are perceived as means of accessing one’s ‘authentic’ self. I analyse the media representations of two transgender celebrities: Caitlyn Jenner and Nadia Almada, alongside the reality TV show TRANSform Me, exploring how these women’s gender transitions have been discursively aligned with a cultural imperative for all women, cisgender or trans, to display their authentic femininity through bodily work. This demonstrates how established tropes of authenticity-via-bodily transformation, have enabled transgender to become culturally legible through the wrong body trope. Problematically, I argue, this process has worked to demarcate ideals of ‘acceptable’ transgender subjectivity: self-sufficient, normatively feminine, and eager to embrace the possibilities for happiness and social integration provided by the commercial domain
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