Numerous nations have prioritised the inclusion of citizens with disabilities, such as hearing loss, in all aspects of social life. Sign language is used by this population, yet they still have trouble communicating with others. Many sign language apps are being created to help bridge the communication gap as a result of technology advances enabled by the widespread use of smartphones. These apps are widely used because they are accessible and inexpensive. The services and capabilities they offer and the quality of their content, however, differ greatly. Evaluation of the quality of the content provided by these applications is necessary if they are to have any kind of real effect. A thorough evaluation like this will inspire developers to work hard on new apps, which will lead to improved software development and experience overall. This research used a systematic literature review (SLR) method, which is recognised in gaining a broad understanding of the study whilst offering additional information for future investigations. SLR was adopted in this research for smartphone-based sign language apps to understand the area and main discussion aspects utilised in the assessment. These studies were reviewed on the basis of related work analysis, main issues, discussions and methodological aspects. Results revealed that the evaluation of sign language mobile apps is scarce. Thus, we proposed a future direction for the quality assessment of these apps. The findings will benefit normal-hearing and hearing-impaired users and open up a new area where researchers and developers could work together on sign language mobile apps. The results will help hearing and non-hearing users and will pave the way for future collaboration between academicians and app developers in the field of sign language technology. 

/pdf/landscape-of-sign-language-research-based-on-smartphone-apps-2qp9swuu.pdf

Landscape of sign language research based on smartphone apps: coherent literature analysis, motivations, open challenges, recommendations and future directions for app assessment

Nowadays, natural language processing techniques enable the development of applications that promote communication between humans and between humans and machines. Although the technology related to automated oral communication is mature and affordable, there are currently no appropriate solutions for visualspatial languages. In the scarce efforts to automatically process sign languages, studies on non-manual gestures are rare, making it difficult to properly interpret the speeches uttered in those languages. In this paper, we present a solution for the automatic segmentation of grammatical facial expressions in sign language. This is a low-cost computational solution designed to integrate a sign language processing framework that supports the development of simple but high value-added applications for the context of universal communication. Moreover, we present a discussion of the difficulties faced by this solution to guide future research in this area.

/pdf/automatic-segmentation-of-grammatical-facial-expressions-in-1n93odq9jc.pdf

Automatic segmentation of grammatical facial expressions in sign language: towards an inclusive communication experience

In recent years, many studies have been conducted for analysis of facial expressions. The importance of facial expressions is particularly evident in sign languages because they help in social communication between deaf and dumb among themselves as well as their interaction with the world. These facial expressions are called Grammatical Facial Expressions (GFEs). In this paper, an overview of several methods used for face tracking based on tracking a set of landmark points projected on the face and also a review of classification techniques that used in order to recognize grammatical expressions. This paper also mentions available datasets that used to facilitate linguistic and computational research on signed languages and the gestural components of spoken languages.

A Survey on Linguistic Interpretation of Facial Expressions and Technologies

The face is the reflection of our emotions. We can guess the state of mind of a person by observing the face. In this paper, we applied an Associative Model algorithm to recognized Grammatical Facial Expressions. We used the dataset of the Brazilian sign language (Libras) system. The model we applied was a Morphological Associative Memory. We implemented a memory for each expression. The average of recognition for the same expression was of 98.89%. When we compare one expression with the others, we obtained a 98.59%, which means that our proposal confuses few expressions.

Gramatical Facial Expression Recognition with Artificial Intelligence Tools

Humans use facial expressions in many contexts to communicate their ideas or weigh their emotions. Deaf people depend on these expressions mainly in daily communications. They use the facial expressions to add the grammatical meaning for sentences of similar words. Therefore, developing smart systems to recognize facial expressions becomes a necessity. The main obstacle comes from the uncertainty and ambiguity of grammatical facial decisions. Hence, fuzzy and fuzzy rough artificial intelligent algorithms formulate feasible solutions to make decisions in such situations. This paper presents a hybrid of fuzzy rough feature selection inspired by ANT Colony Optimization (FRFS-ACO) and fuzzy rough nearest neighbor (FRNN) classification algorithms to decide about different facial expressions. The proposed hybrid is compared to other artificial algorithms and hybrids to judge its accuracy and efficiency. The experiments are accomplished using a standard grammatical facial expressions data set with nine different emotions recorded by Microsoft Kinect sensor and kept on the UCI machine learning repository. The experiments and comparisons clarified that the proposed hybrid provide feasible average accuracy level of 93.7% and dramatically reduced the required classification time.

Grammatical Facial Expression Recognition Basing on a Hybrid of Fuzzy Rough Ant Colony Optimization and Nearest Neighbor Classifier

The proposed research explores a relatively new area of expression detection through facial points in a sign language to enhance the computer interaction with the deaf and hard of hearing. The research mainly focuses on facial points collected from Kinect as basis for expression detection as opposed to numerous gesture based studies on sign language. This helps in deploying the applications in smart phones as it is feasible to capture facial point easily rather than hand gestures. Exhaustive experimentation is carried out with ten different machine learning algorithms for detecting nine different types of expression modeled as different binary classification problem for each expression. This is done for user dependent model and user independent model scenarios. The optimal classifier for each expression is found to outperform the current state-of-the-art techniques and has ROC area greater than 0.95 for each expression. It is found that user independent model's performance is comparable to user dependent model, hence is suggested as it is easy and efficient to deploy in practical applications. Finally, the importance of each facial point in detecting each type of expression has been mined, which can be instrumental for future research and for various application using facial points as basis for decision making.

Detection and analysis model for grammatical facial expressions in sign language

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Detection and analysis model for grammatical facial expressions in sign language