Recognition of Facial Expressions Using Geometric Appearance Features

This work introduces an autonomous face emotion identification system that utilizes geometric appearance features. By embracing facial expression recognition technology, businesses can navigate the contours of the new world of work and build sustainable practices. This paper highlights the potential of combined features for facial expression recognition and emphasizes the importance of leveraging such technology in shaping the future of sustainable business. The experiment results obtained from the MUG database, using an ensemble bagging technique, demonstrate a high accuracy rate of 94% when employing the combined features. It sheds light on the opportunities and challenges associated with implementing these systems and provide insights into how organizations can integrate them into their operations effectively. Ultimately, this paper presents a transformative approach to understanding human emotions in the context of the changing global landscape.