23,287 research outputs found
Face Mask Extraction in Video Sequence
Inspired by the recent development of deep network-based methods in semantic image segmentation, we introduce an end-to-end trainable model for face mask extraction in video sequence. Comparing to landmark-based sparse face shape representation, our method can produce the segmentation masks of individual facial components, which can better reflect their detailed shape variations. By integrating Convolutional LSTM (ConvLSTM) algorithm with Fully Convolutional Networks (FCN), our new ConvLSTM-FCN model works on a per-sequence basis and takes advantage of the temporal correlation in video clips. In addition, we also propose a novel loss function, called Segmentation Loss, to directly optimise the Intersection over Union (IoU) performances. In practice, to further increase segmentation accuracy, one primary model and two additional models were trained to focus on the face, eyes, and mouth regions, respectively. Our experiment shows the proposed method has achieved a 16.99% relative improvement (from 54.50% to 63.76% mean IoU) over the baseline FCN model on the 300 Videos in the Wild (300VW) dataset
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics
Smile is one of the key elements in identifying emotions and present state of
mind of an individual. In this work, we propose a cluster of approaches to
classify posed and spontaneous smiles using deep convolutional neural network
(CNN) face features, local phase quantization (LPQ), dense optical flow and
histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for
micro-expression smile amplification along with three normalization procedures
for distinguishing posed and spontaneous smiles. Although the deep CNN face
model is trained with large number of face images, HOG features outperforms
this model for overall face smile classification task. Using EVM to amplify
micro-expressions did not have a significant impact on classification accuracy,
while the normalizing facial features improved classification accuracy. Unlike
many manual or semi-automatic methodologies, our approach aims to automatically
classify all smiles into either `spontaneous' or `posed' categories, by using
support vector machines (SVM). Experimental results on large UvA-NEMO smile
database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial
Behavior Analysi
FaceFilter: Audio-visual speech separation using still images
The objective of this paper is to separate a target speaker's speech from a
mixture of two speakers using a deep audio-visual speech separation network.
Unlike previous works that used lip movement on video clips or pre-enrolled
speaker information as an auxiliary conditional feature, we use a single face
image of the target speaker. In this task, the conditional feature is obtained
from facial appearance in cross-modal biometric task, where audio and visual
identity representations are shared in latent space. Learnt identities from
facial images enforce the network to isolate matched speakers and extract the
voices from mixed speech. It solves the permutation problem caused by swapped
channel outputs, frequently occurred in speech separation tasks. The proposed
method is far more practical than video-based speech separation since user
profile images are readily available on many platforms. Also, unlike
speaker-aware separation methods, it is applicable on separation with unseen
speakers who have never been enrolled before. We show strong qualitative and
quantitative results on challenging real-world examples.Comment: Under submission as a conference paper. Video examples:
https://youtu.be/ku9xoLh62
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