The models achieved good performance, reaching 97% accuracy on the extended Cohn-Kanade (CK+) dataset with a k-nearest neighbor (KNN) classifier and real-time processing time of 250 Hz. This study's proposed approach relies on a graph convolution network (GCN) as the classification model. The GCN adopts a spatially unconstrained methodology, allowing points to exist freely in three-dimensional space. Unlike methods constrained to a specific plane, the GCN leverages the interconnections among linkages and establishes relations with point anchors, resulting in a more resilient and robust outcome [31]. Graph networkscan be used in a wide array of applications from generative models and traffic network predictions [32] to text sequence labeling [33]. To evaluate the proposed approach, the Aff-Wild2 [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44] database was employed. This database is considered large and is composed of in-the-wild, e.g., non-posed, captured image frames. The images were first passed through the point landmark feature extractor of Mediapipe [45], where 478 facial landmarks were extracted and depicted in a 3D coordinate plane. A subset of Aff-Wild2 was used for the analysis of the proposed approach. The subset was split according to an 80% training and 20% validation partition. As Aff-Wild2 is a challenge database, different techniques and approaches have been studied for emotion recognition. In [46], a semi-supervised approach to improve facial expression recognition was implemented using unlabeled data and a dynamic threshold module, achieving an F1-score of 0.3075 [44]. An expression-related self-supervised learning method was developed in [47] to classify facial expressions, achieving an F1-score of 0.3218 [44]. A multi-layered perceptron ensemble was studied in [48] with a pre-trained EmotiEffNet architecture for feature extraction from frames. This approach achieved an F1-score of 0.3292 [44]. A fused transformer encoder model using audio-visual input with an affine module was implemented in [49], reaching an F1-score of 0.3337 [44]. In [50], a multi-modal fusion model was developed. This approach leverages a temporal convolutional network and transformer models to enhance performance, reaching an F1-score of 0.3532 [44]. Specifically, the study in this work leverages the unique characteristics of facial meshes and the interrelations among facial landmarks, which is an approach which, to the best of the authors' knowledge, has not been extensively explored in prior research. In particular, while previous studies touched on the use of a fraction of the 478 facial landmarks with a distinct relational geometry, there is an opportunity in this work to delve deeper into capturing subtle changes in facial expressions. These subtle changes are often overlooked in pursuit of broader emotional generalizations but may contain key elements to improve the overall emotion recognition capability. This study's primary aim is to show that combining facial landmark points with a graph convolution network provides better efficacy in identifying emotions. Section 2 describes the methods used for the network architecture, feature selection, and analysis criteria. Section 3 provides the results, followed by their respective discussions in Section 4, and concluding with a summary of the main findings in Section 5.