Segregation of meaningful strokes, a pre‐requisite for self co‐articulation removal in isolated dynamic gestures

• Main Authors: Anish Monsley K., Kuldeep Singh Yadav, Songhita Misra, Taimoor Khan, M. K. Bhuyan, Rabul Hussain Laskar
• Format: Article
• Language: English
• Published: Wiley 2021-04-01
• Series: IET Image Processing
• Online Access: https://doi.org/10.1049/ipr2.12095
• ISSN: 1751-9659; 1751-9667

Abstract Gesture formation, a pre‐processing step, has its importance when variations in patterns, scale, and speed come into play. Self co‐articulations are intentional movements performed by an individual to complete a gesture, whose presence in the trajectory alters its original meaning. For recognition, most researchers have directly used the trajectory formed along with these self co‐articulated strokes, with a few removing it using visible trait‐like velocity. Usage of velocity has shortcomings as gesturing in air differs from gesturing over a solid surface; hence, we propose a gesture formation model, which incorporates global and local measures to remove these self co‐articulations. The global measure uses Euclidean distance, instantaneous velocity, and polarity calculated from the complete gesture, while the local measure segments the gesture into stroke‐level segments by using the minimum–maximum‐polarity algorithm and applies the selective bypass rules. The proposed model, when experimented on gestures patterns with premeditated speed variation, has a mean error rate of 0.0069 and 7.40% self co‐articulations;individuals’ natural gesticulation has a mean error rate of 0.0371 and 12.07% self co‐articulations. Experimentation on each gesture of NITS hand gesture databases showed a relative improvement of 40% (accuracy 97%) over the existing baseline models.