Processing Real-Life Recordings of Facial Expressions of Polish Sign Language Using Action Units

Abstract

Automatic translation between the national language and sign language is a complex process similar to translation between two different foreign languages. A very important aspect is the precision of not only manual gestures but also facial expressions, which are extremely important in the overall context of a sentence. In this article, we present the problem of including facial expressions in the automation of Polish-to-Polish Sign Language (PJM) translation—this is part of an ongoing project related to a comprehensive solution allowing for the animation of manual gestures, body movements and facial expressions. Our approach explores the possibility of using action unit (AU) recognition in the automatic annotation of recordings, which in the subsequent steps will be used to train machine learning models. This paper aims to evaluate entropy in real-life translation recordings and analyze the data associated with the detected action units. Our approach has been subjected to evaluation by experts related to Polish Sign Language, and the results obtained allow for the development of further work related to automatic translation into Polish Sign Language.

Enhancing Signer-Independent Recognition of Isolated Sign Language through Advanced Deep Learning Techniques and Feature Fusion

“…In particular, signer differences between signers complicate the recognition process, so the developed systems must perform with high accuracy while being signer-independent [25]. A complete SLR system should consider facial expressions [26,27], body language, and hand gestures since a holistic evaluation of these features is essential for full SLR [28,29]. The dynamic nature of sign language requires integrating both spatial and temporal dimensions of the data, which necessitates the creation of efficient models that can capture both spatial and temporal information.…”