Efficient Lightweight 3D-CNN using Frame Skipping and Contrast Enhancement for Facial Macro- and Micro-expression Spotting

Micro-expression spotting is the preliminary step for any micro-expression related analysis to avoid excessive false positives. We propose an efficient lightweight macro- and micro-expression spotting method which takes advantage of the duration differences of macro- and micro-expressions. Using effective frame skips, local contrast normalisation, depthwise separable convolutions and residual connections, we construct Efficient Lightweight 3D-Convolutional Network using Frame Skipping and Contrast Enhancement (EL-FACE) for the micro-expression spotting task. Our model achieves state-of-the-art performance in SAMM Long Videos and remained competitive in the CAS(ME)2 dataset.