Gene

Quantum mechanics

CLIPS

Transfer of learning

Statistics

Chromatography

Expression (computer science)

ENCODE

Telecommunications

Biochemistry

Computer science

Physics

Pattern recognition (psychology)

Mathematics

Artificial intelligence

Sample size determination

Chemistry

Programming language

Deep learning

Sample (material)

Term (time)

Artificial neural network

Convolutional neural network

Recurrent neural network

Frame (networking)

Neurocomputing

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.

Micro-expression recognition with small sample size by transferring long-term convolutional neural network