1,584 research outputs found
Smile detection in the wild based on transfer learning
Smile detection from unconstrained facial images is a specialized and
challenging problem. As one of the most informative expressions, smiles convey
basic underlying emotions, such as happiness and satisfaction, which lead to
multiple applications, e.g., human behavior analysis and interactive
controlling. Compared to the size of databases for face recognition, far less
labeled data is available for training smile detection systems. To leverage the
large amount of labeled data from face recognition datasets and to alleviate
overfitting on smile detection, an efficient transfer learning-based smile
detection approach is proposed in this paper. Unlike previous works which use
either hand-engineered features or train deep convolutional networks from
scratch, a well-trained deep face recognition model is explored and fine-tuned
for smile detection in the wild. Three different models are built as a result
of fine-tuning the face recognition model with different inputs, including
aligned, unaligned and grayscale images generated from the GENKI-4K dataset.
Experiments show that the proposed approach achieves improved state-of-the-art
performance. Robustness of the model to noise and blur artifacts is also
evaluated in this paper
Every Smile is Unique: Landmark-Guided Diverse Smile Generation
Each smile is unique: one person surely smiles in different ways (e.g.,
closing/opening the eyes or mouth). Given one input image of a neutral face,
can we generate multiple smile videos with distinctive characteristics? To
tackle this one-to-many video generation problem, we propose a novel deep
learning architecture named Conditional Multi-Mode Network (CMM-Net). To better
encode the dynamics of facial expressions, CMM-Net explicitly exploits facial
landmarks for generating smile sequences. Specifically, a variational
auto-encoder is used to learn a facial landmark embedding. This single
embedding is then exploited by a conditional recurrent network which generates
a landmark embedding sequence conditioned on a specific expression (e.g.,
spontaneous smile). Next, the generated landmark embeddings are fed into a
multi-mode recurrent landmark generator, producing a set of landmark sequences
still associated to the given smile class but clearly distinct from each other.
Finally, these landmark sequences are translated into face videos. Our
experimental results demonstrate the effectiveness of our CMM-Net in generating
realistic videos of multiple smile expressions.Comment: Accepted as a poster in Conference on Computer Vision and Pattern
Recognition (CVPR), 201
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
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