5 research outputs found
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
In the recent year, state-of-the-art for facial micro-expression recognition
have been significantly advanced by deep neural networks. The robustness of
deep learning has yielded promising performance beyond that of traditional
handcrafted approaches. Most works in literature emphasized on increasing the
depth of networks and employing highly complex objective functions to learn
more features. In this paper, we design a Shallow Triple Stream
Three-dimensional CNN (STSTNet) that is computationally light whilst capable of
extracting discriminative high level features and details of micro-expressions.
The network learns from three optical flow features (i.e., optical strain,
horizontal and vertical optical flow fields) computed based on the onset and
apex frames of each video. Our experimental results demonstrate the
effectiveness of the proposed STSTNet, which obtained an unweighted average
recall rate of 0.7605 and unweighted F1-score of 0.7353 on the composite
database consisting of 442 samples from the SMIC, CASME II and SAMM databases.Comment: 5 pages, 1 figure, Accepted and published in IEEE FG 201
A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
The automatic recognition of micro-expression has been boosted ever since the
successful introduction of deep learning approaches. As researchers working on
such topics are moving to learn from the nature of micro-expression, the
practice of using deep learning techniques has evolved from processing the
entire video clip of micro-expression to the recognition on apex frame. Using
the apex frame is able to get rid of redundant video frames, but the relevant
temporal evidence of micro-expression would be thereby left out. This paper
proposes a novel Apex-Time Network (ATNet) to recognize micro-expression based
on spatial information from the apex frame as well as on temporal information
from the respective-adjacent frames. Through extensive experiments on three
benchmarks, we demonstrate the improvement achieved by learning such temporal
information. Specially, the model with such temporal information is more robust
in cross-dataset validations.Comment: 6 pages, 3 figures, 3 tables, code available, accepted in ACII 201
Micro-expression recognition with small sample size by transferring long-term convolutional neural network
Abstract
Micro-expression is one of important clues for detecting lies. Its most outstanding characteristics include short duration and low intensity of movement. Therefore, video clips of high spatial-temporal resolution are much more desired than still images to provide sufficient details. On the other hand, owing to the difficulties to collect and encode micro-expression data, it is small sample size. In this paper, we use only 560 micro-expression video clips to evaluate the proposed network model: Transferring Long-term Convolutional Neural Network (TLCNN). TLCNN uses Deep CNN to extract features from each frame of micro-expression video clips, then feeds them to Long Short Term Memory (LSTM) which learn the temporal sequence information of micro-expression. Due to the small sample size of micro-expression data, TLCNN uses two steps of transfer learning: (1) transferring from expression data and (2) transferring from single frame of micro-expression video clips, which can be regarded as “big data”. Evaluation on 560 micro-expression video clips collected from three spontaneous databases is performed. The results show that the proposed TLCNN is better than some state-of-the-art algorithms