Optical strain is an extension of optical flow that is capable of quantifying subtle changes on faces and representing the minute facial motion intensities at the pixel level. This is computationally essential for the relatively new field of spontaneous micro-expression, where subtle expressions can be technically challenging to pinpoint. In this paper, we present a novel method for detecting and recognizing micro-expressions by utilizing facial optical strain magnitudes to construct optical strain features and optical strain weighted features. The two sets of features are then concatenated to form the resultant feature histogram. Experiments were performed on the CASME II and SMIC databases. We demonstrate on both databases, the usefulness of optical strain information and more importantly, that our best approaches are able to outperform the original baseline results for both detection and recognition tasks. A comparison of the proposed method with other existing spatio-temporal feature extraction approaches is also presented. HighlightsThe method proposed is a combination of two optical strain derived features.Optical strain magnitudes were employed to describe fine subtle facial movements.Evaluation was performed in both the detection and recognition tasks.Promising performances were obtained in two micro-expression databases.
In the paper by @cite_4 , optical strain pattern was used for spotting facial micro-expressions automatically. They achieved 100 , the dataset used possess a small sample size, and contains a total of only 7 micro-expressions. Besides, the micro-expressions detected were not spontaneous but rather posed ones, which are less natural and realistic.
@cite_4
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This paper presents a novel method for automatic spotting (temporal segmentation) of facial expressions in long videos comprising of continuous and changing expressions. The method utilizes the strain impacted on the facial skin due to the non-rigid motion caused during expressions. The strain magnitude is calculated using the central difference method over the robust and dense optical flow field of each subjects face. Testing has been done on 2 datasets (which includes 100 macro-expressions) and promising results have been obtained. The method is robust to several common drawbacks found in automatic facial expression segmentation including moderate in-plane and out-of-plane motion. Additionally, the method has also been modified to work with videos containing micro-expressions. Micro-expressions are detected utilizing their smaller spatial and temporal extent. A subject's face is divided in to sub-regions (mouth, cheeks, forehead, and eyes) and facial strain is calculated for each of these regions. Strain patterns in individual regions are used to identify subtle changes which facilitate the detection of micro-expressions. Optical strain characterizes the relative amount of displacement by a moving object within a time interval. Its ability to compute any small muscular movements on faces can be advantageous to subtle expression research. This paper proposes a novel optical strain weighted feature extraction scheme for subtle facial micro-expression recognition. Motion information is derived from optical strain magnitudes, which is then pooled spatio-temporally to obtain block-wise weights for the spatial image plane. By simple product with the weights, the resulting feature histograms are intuitively scaled to accommodate the importance of block regions. Experiments conducted on two recent spontaneous micro-expression databases–CASMEII and SMIC, demonstrated promising improvement over the baseline results. Spontaneous facial micro-expression analysis has become an active task for recognizing suppressed and involuntary facial expressions shown on the face of humans. Recently, Local Binary Pattern from Three Orthogonal Planes (LBP-TOP) has been employed for micro-expression analysis. However, LBP-TOP suffers from two critical problems, causing a decrease in the performance of micro-expression analysis. It generally extracts appearance and motion features from the sign-based difference between two pixels but not yet considers other useful information. As well, LBP-TOP commonly uses classical pattern types which may be not optimal for local structure in some applications. This paper proposes SpatioTemporal Completed Local Quantization Patterns (STCLQP) for facial micro-expression analysis. Firstly, STCLQP extracts three interesting information containing sign, magnitude and orientation components. Secondly, an efficient vector quantization and codebook selection are developed for each component in appearance and temporal domains to learn compact and discriminative codebooks for generalizing classical pattern types. Finally, based on discriminative codebooks, spatiotemporal features of sign, magnitude and orientation components are extracted and fused. Experiments are conducted on three publicly available facial micro-expression databases. Some interesting findings about the neighboring patterns and the component analysis are concluded. Comparing with the state of the art, experimental results demonstrate that STCLQP achieves a substantial improvement for analyzing facial micro-expressions. HighlightsWe propose spatiotemporal completed local quantized pattern for micro-expression analysis.We propose to use three useful information, including the sign-based, magnitude-based and orientation-based difference of pixels for LBP.We propose to use an efficient vector quantization and discriminative codebook selection to make LBP-TOP more discriminative and compact.We evaluate the framework on three publicly available facial micro-expression databases.We evaluate the influence of parameters, different components and codebook selection to spatiotemporal completed local quantized pattern. The automatic recognition of spontaneous facial micro-expressions becomes prevalent as it reveals the actual emotion of humans. However, handcrafted features employed for recognizing micro-expressions are designed for general applications and thus cannot well capture the subtle facial deformations of micro-expressions. To address this problem, we propose an end-to-end deep learning framework to suit the particular needs of micro-expression recognition (MER). In the deep model, re- current convolutional networks are utilized to learn the representation of subtle changes from image sequences. To guarantee the learning of deep model, we present a temporal jittering procedure to greatly enrich the training samples. Through performing the experiments on three spontaneous micro-expression datasets, i.e., SMIC, CASME, and CASME2, we verify the effectiveness of our proposed MER approach.