Despite recent interest and advances in facial micro-expression research, there is still plenty of room for improvement in terms of micro-expression recognition. Conventional feature extraction approaches for micro-expression video consider either the whole video sequence or a part of it, for representation. However, with the high-speed video capture of microexpressions (100-200 fps), are all frames necessary to provide a sufficiently meaningful representation? Is the luxury of data a bane to accurate recognition? A novel proposition is presented in this paper, whereby we utilize only two images per video, namely, the apex frame and the onset frame. The apex frame of a video contains the highest intensity of expression changes among all frames, while the onset is the perfect choice of a reference frame with neutral expression. A new feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to encode essential expressiveness of the apex frame. We evaluated the proposed method on five micro-expression databases-CAS(ME) 2 , CASME II, SMIC-HS, SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with our proposed technique achieving a state-of-the-art F1-score recognition performance of 0.61 and 0.62 in the high frame rate CASME II and SMIC-HS databases respectively.

In the recent year, state-of-the-art for facial microexpression 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.

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Micro-expression usually occurs at high-stakes situations and may provide useful information in the field of behavioral psychology for better interpretion and analysis. Unfortunately, it is technically challenging to detect and recognize micro-expressions due to its brief duration and the subtle facial distortions. Apex frame, which is the instant indicating the most expressive emotional state in a video, is effective to classify the emotion in that particular frame. In this work, we present a novel method to spot the apex frame of a spontaneous micro-expression video sequence. A binary search approach is employed to locate the index of the frame in which the peak facial changes occur. Features from specific facial regions are extracted to better represent and describe the expression details. The defined facial regions are selected based on the action unit and landmark coordinates of the subject, in which case these processes are automated. We consider three distinct feature descriptors to evaluate the reliability of the proposed approach. Improvements of at least 20% are achieved when compared to the baselines.

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