181,464 research outputs found
In-the-wild Facial Expression Recognition in Extreme Poses
In the computer research area, facial expression recognition is a hot
research problem. Recent years, the research has moved from the lab environment
to in-the-wild circumstances. It is challenging, especially under extreme
poses. But current expression detection systems are trying to avoid the pose
effects and gain the general applicable ability. In this work, we solve the
problem in the opposite approach. We consider the head poses and detect the
expressions within special head poses. Our work includes two parts: detect the
head pose and group it into one pre-defined head pose class; do facial
expression recognize within each pose class. Our experiments show that the
recognition results with pose class grouping are much better than that of
direct recognition without considering poses. We combine the hand-crafted
features, SIFT, LBP and geometric feature, with deep learning feature as the
representation of the expressions. The handcrafted features are added into the
deep learning framework along with the high level deep learning features. As a
comparison, we implement SVM and random forest to as the prediction models. To
train and test our methodology, we labeled the face dataset with 6 basic
expressions.Comment: Published on ICGIP201
Towards robust and reliable multimedia analysis through semantic integration of services
Thanks to ubiquitous Web connectivity and portable multimedia devices, it has never been so easy to produce and distribute new multimedia resources such as videos, photos, and audio. This ever-increasing production leads to an information overload for consumers, which calls for efficient multimedia retrieval techniques. Multimedia resources can be efficiently retrieved using their metadata, but the multimedia analysis methods that can automatically generate this metadata are currently not reliable enough for highly diverse multimedia content. A reliable and automatic method for analyzing general multimedia content is needed. We introduce a domain-agnostic framework that annotates multimedia resources using currently available multimedia analysis methods. By using a three-step reasoning cycle, this framework can assess and improve the quality of multimedia analysis results, by consecutively (1) combining analysis results effectively, (2) predicting which results might need improvement, and (3) invoking compatible analysis methods to retrieve new results. By using semantic descriptions for the Web services that wrap the multimedia analysis methods, compatible services can be automatically selected. By using additional semantic reasoning on these semantic descriptions, the different services can be repurposed across different use cases. We evaluated this problem-agnostic framework in the context of video face detection, and showed that it is capable of providing the best analysis results regardless of the input video. The proposed methodology can serve as a basis to build a generic multimedia annotation platform, which returns reliable results for diverse multimedia analysis problems. This allows for better metadata generation, and improves the efficient retrieval of multimedia resources
Robust Modeling of Epistemic Mental States
This work identifies and advances some research challenges in the analysis of
facial features and their temporal dynamics with epistemic mental states in
dyadic conversations. Epistemic states are: Agreement, Concentration,
Thoughtful, Certain, and Interest. In this paper, we perform a number of
statistical analyses and simulations to identify the relationship between
facial features and epistemic states. Non-linear relations are found to be more
prevalent, while temporal features derived from original facial features have
demonstrated a strong correlation with intensity changes. Then, we propose a
novel prediction framework that takes facial features and their nonlinear
relation scores as input and predict different epistemic states in videos. The
prediction of epistemic states is boosted when the classification of emotion
changing regions such as rising, falling, or steady-state are incorporated with
the temporal features. The proposed predictive models can predict the epistemic
states with significantly improved accuracy: correlation coefficient (CoERR)
for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for
Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special
Issue: Socio-Affective Technologie
Deep Learning Face Attributes in the Wild
Predicting face attributes in the wild is challenging due to complex face
variations. We propose a novel deep learning framework for attribute prediction
in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly
with attribute tags, but pre-trained differently. LNet is pre-trained by
massive general object categories for face localization, while ANet is
pre-trained by massive face identities for attribute prediction. This framework
not only outperforms the state-of-the-art with a large margin, but also reveals
valuable facts on learning face representation.
(1) It shows how the performances of face localization (LNet) and attribute
prediction (ANet) can be improved by different pre-training strategies.
(2) It reveals that although the filters of LNet are fine-tuned only with
image-level attribute tags, their response maps over entire images have strong
indication of face locations. This fact enables training LNet for face
localization with only image-level annotations, but without face bounding boxes
or landmarks, which are required by all attribute recognition works.
(3) It also demonstrates that the high-level hidden neurons of ANet
automatically discover semantic concepts after pre-training with massive face
identities, and such concepts are significantly enriched after fine-tuning with
attribute tags. Each attribute can be well explained with a sparse linear
combination of these concepts.Comment: To appear in International Conference on Computer Vision (ICCV) 201
- …