297 research outputs found
Towards a reliable face recognition system.
Face Recognition (FR) is an important area in computer vision with many applications such as security and automated border controls. The recent advancements in this domain have pushed the performance of models to human-level accuracy. However, the varying conditions in the real-world expose more challenges for their adoption. In this paper, we investigate the performance of these models. We analyze the performance of a cross-section of face detection and recognition models. Experiments were carried out without any preprocessing on three state-of-the-art face detection methods namely HOG, YOLO and MTCNN, and three recognition models namely, VGGface2, FaceNet and Arcface. Our results indicated that there is a significant reliance by these methods on preprocessing for optimum performance
Stacking-fault energies for Ag, Cu, and Ni from empirical tight-binding potentials
The intrinsic stacking-fault energies and free energies for Ag, Cu, and Ni
are derived from molecular-dynamics simulations using the empirical
tight-binding potentials of Cleri and Rosato [Phys. Rev. B 48, 22 (1993)].
While the results show significant deviations from experimental data, the
general trend between the elements remains correct. This allows to use the
potentials for qualitative comparisons between metals with high and low
stacking-fault energies. Moreover, the effect of stacking faults on the local
vibrational properties near the fault is examined. It turns out that the
stacking fault has the strongest effect on modes in the center of the
transverse peak and its effect is localized in a region of approximately eight
monolayers around the defect.Comment: 5 pages, 2 figures, accepted for publication in Phys. Rev.
Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution
Given a set of images containing objects from the same category, the task of
image co-localization is to identify and localize each instance. This paper
shows that this problem can be solved by a simple but intriguing idea, that is,
a common object detector can be learnt by making its detection confidence
scores distributed like those of a strongly supervised detector. More
specifically, we observe that given a set of object proposals extracted from an
image that contains the object of interest, an accurate strongly supervised
object detector should give high scores to only a small minority of proposals,
and low scores to most of them. Thus, we devise an entropy-based objective
function to enforce the above property when learning the common object
detector. Once the detector is learnt, we resort to a segmentation approach to
refine the localization. We show that despite its simplicity, our approach
outperforms state-of-the-art methods.Comment: Accepted to Proc. European Conf. Computer Vision 201
Multi-view Face Detection Using Deep Convolutional Neural Networks
In this paper we consider the problem of multi-view face detection. While
there has been significant research on this problem, current state-of-the-art
approaches for this task require annotation of facial landmarks, e.g. TSM [25],
or annotation of face poses [28, 22]. They also require training dozens of
models to fully capture faces in all orientations, e.g. 22 models in HeadHunter
method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method
that does not require pose/landmark annotation and is able to detect faces in a
wide range of orientations using a single model based on deep convolutional
neural networks. The proposed method has minimal complexity; unlike other
recent deep learning object detection methods [9], it does not require
additional components such as segmentation, bounding-box regression, or SVM
classifiers. Furthermore, we analyzed scores of the proposed face detector for
faces in different orientations and found that 1) the proposed method is able
to detect faces from different angles and can handle occlusion to some extent,
2) there seems to be a correlation between dis- tribution of positive examples
in the training set and scores of the proposed face detector. The latter
suggests that the proposed methods performance can be further improved by using
better sampling strategies and more sophisticated data augmentation techniques.
Evaluations on popular face detection benchmark datasets show that our
single-model face detector algorithm has similar or better performance compared
to the previous methods, which are more complex and require annotations of
either different poses or facial landmarks.Comment: in International Conference on Multimedia Retrieval 2015 (ICMR
Development of a tight-binding potential for bcc-Zr. Application to the study of vibrational properties
We present a tight-binding potential based on the moment expansion of the
density of states, which includes up to the fifth moment. The potential is
fitted to bcc and hcp Zr and it is applied to the computation of vibrational
properties of bcc-Zr. In particular, we compute the isothermal elastic
constants in the temperature range 1200K < T < 2000K by means of standard Monte
Carlo simulation techniques. The agreement with experimental results is
satisfactory, especially in the case of the stability of the lattice with
respect to the shear associated with C'. However, the temperature decrease of
the Cauchy pressure is not reproduced. The T=0K phonon frequencies of bcc-Zr
are also computed. The potential predicts several instabilities of the bcc
structure, and a crossing of the longitudinal and transverse modes in the (001)
direction. This is in agreement with recent ab initio calculations in Sc, Ti,
Hf, and La.Comment: 14 pages, 6 tables, 4 figures, revtex; the kinetic term of the
isothermal elastic constants has been corrected (Eq. (4.1), Table VI and
Figure 4
Probabilistic Computation in Human Perception under Variability in Encoding Precision
A key function of the brain is to interpret noisy sensory information. To do so optimally, observers must, in many tasks, take into account knowledge of the precision with which stimuli are encoded. In an orientation change detection task, we find that encoding precision does not only depend on an experimentally controlled reliability parameter (shape), but also exhibits additional variability. In spite of variability in precision, human subjects seem to take into account precision near-optimally on a trial-to-trial and item-to-item basis. Our results offer a new conceptualization of the encoding of sensory information and highlight the brain’s remarkable ability to incorporate knowledge of uncertainty during complex perceptual decision-making
Robust averaging protects decisions from noise in neural computations
An ideal observer will give equivalent weight to sources of information that are equally reliable. However, when averaging visual information, human observers tend to downweight or discount features that are relatively outlying or deviant (‘robust averaging’). Why humans adopt an integration policy that discards important decision information remains unknown. Here, observers were asked to judge the average tilt in a circular array of high-contrast gratings, relative to an orientation boundary defined by a central reference grating. Observers showed robust averaging of orientation, but the extent to which they did so was a positive predictor of their overall performance. Using computational simulations, we show that although robust averaging is suboptimal for a perfect integrator, it paradoxically enhances performance in the presence of “late” noise, i.e. which corrupts decisions during integration. In other words, robust decision strategies increase the brain’s resilience to noise arising in neural computations during decision-making
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