We present a novel boundary-aware face alignment algorithm by utilising boundary lines as the geometric structure of a human face to help facial landmark localisation. Unlike the conventional heatmap based method and regression based method, our approach derives face landmarks from boundary lines which remove the ambiguities in the landmark definition. Three questions are explored and answered by this work: 1. Why using boundary? 2. How to use boundary? 3. What is the relationship between boundary estimation and landmarks localisation? Our boundaryaware face alignment algorithm achieves 3.49% mean error on 300-W Fullset, which outperforms state-of-the-art methods by a large margin. Our method can also easily integrate information from other datasets. By utilising boundary information of 300-W dataset, our method achieves 3.92% mean error with 0.39% failure rate on COFW dataset, and 1.25% mean error on AFLW-Full dataset. Moreover, we propose a new dataset WFLW to unify training and testing across different factors, including poses, expressions, illuminations, makeups, occlusions, and blurriness. Dataset and model will be publicly available at https://wywu. github.io/projects/LAB/LAB.html

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The development of intelligent information technology provides an effective way for cross-modal learning analytics and promotes the realization of procedural and scientific educational evaluation. To accurately capture the emotional changes of learners and make an accurate evaluation of the learning process, this paper takes the evaluation of learners’ emotional status in the behavior process as an example to construct an intelligent analysis model of learners’ emotional status in the behavior process, which provides effective technical solutions for the construction of cross-modal learning analytics. The effectiveness and superiority of the proposed method are verified by experiments. Through the analysis of learners’ cross-mode learning behavior, it innovates the evaluation method of classroom teaching in the intelligent age and improves the quality of modern teaching.