Facial emotion recognition (FER) has been an active research topic in the past several years. One of difficulties in FER is the effective capture of geometrical and temporary information from landmarks. In this paper, we propose a graph convolution neural network that utilizes landmark features for FER, which we called a directed graph neural network (DGNN). Nodes in the graph structure were defined by landmarks, and edges in the directed graph were built by the Delaunay method. By using graph neural networks, we could capture emotional information through faces’ inherent properties, like geometrical and temporary information. Also, in order to prevent the vanishing gradient problem, we further utilized a stable form of a temporal block in the graph framework. Our experimental results proved the effectiveness of the proposed method for datasets such as CK+ (96.02%), MMI (69.4%), and AFEW (32.64%). Also, a fusion network using image information as well as landmarks, is presented and investigated for the CK+ (98.47% performance) and AFEW (50.65% performance) datasets.

Conventional single-stage object detectors have been able to efficiently detect objects of various sizes using a feature pyramid network. However, because they adopt a too simple manner of aggregating feature maps, they cannot avoid performance degradation due to information loss. To solve this problem, this paper proposes a new framework for single-stage object detection. The proposed aggregation scheme introduces two independent modules to extract global and local information. First, the global information extractor is designed so that each feature vector can reflect the information of the entire image through a non-local neural network (NLNN). Next, the local information extractor aggregates each feature map more effectively through the improved bi-directional network. The proposed method can achieve better performance than the existing single-stage object detection methods by providing improved feature maps to the detection heads. For example, the proposed method shows 1.6% higher average precision (AP) than the efficient featurized image pyramid network (EFIPNet) for the MicroSoft Common Objects in COntext (MS COCO) dataset.