16,724 research outputs found
Hand2Face: Automatic Synthesis and Recognition of Hand Over Face Occlusions
A person's face discloses important information about their affective state.
Although there has been extensive research on recognition of facial
expressions, the performance of existing approaches is challenged by facial
occlusions. Facial occlusions are often treated as noise and discarded in
recognition of affective states. However, hand over face occlusions can provide
additional information for recognition of some affective states such as
curiosity, frustration and boredom. One of the reasons that this problem has
not gained attention is the lack of naturalistic occluded faces that contain
hand over face occlusions as well as other types of occlusions. Traditional
approaches for obtaining affective data are time demanding and expensive, which
limits researchers in affective computing to work on small datasets. This
limitation affects the generalizability of models and deprives researchers from
taking advantage of recent advances in deep learning that have shown great
success in many fields but require large volumes of data. In this paper, we
first introduce a novel framework for synthesizing naturalistic facial
occlusions from an initial dataset of non-occluded faces and separate images of
hands, reducing the costly process of data collection and annotation. We then
propose a model for facial occlusion type recognition to differentiate between
hand over face occlusions and other types of occlusions such as scarves, hair,
glasses and objects. Finally, we present a model to localize hand over face
occlusions and identify the occluded regions of the face.Comment: Accepted to International Conference on Affective Computing and
Intelligent Interaction (ACII), 201
Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks
This work shows that it is possible to fool/attack recent state-of-the-art
face detectors which are based on the single-stage networks. Successfully
attacking face detectors could be a serious malware vulnerability when
deploying a smart surveillance system utilizing face detectors. We show that
existing adversarial perturbation methods are not effective to perform such an
attack, especially when there are multiple faces in the input image. This is
because the adversarial perturbation specifically generated for one face may
disrupt the adversarial perturbation for another face. In this paper, we call
this problem the Instance Perturbation Interference (IPI) problem. This IPI
problem is addressed by studying the relationship between the deep neural
network receptive field and the adversarial perturbation. As such, we propose
the Localized Instance Perturbation (LIP) that uses adversarial perturbation
constrained to the Effective Receptive Field (ERF) of a target to perform the
attack. Experiment results show the LIP method massively outperforms existing
adversarial perturbation generation methods -- often by a factor of 2 to 10.Comment: to appear ECCV 2018 (accepted version
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