Enhancing Image Clarity: Feature Selection with Trickster Coyote Optimization in Noisy/Blurry Images

Abstract

This paper presents a novel method for recognizing human emotions from gait data collected in an unconstrained environment. The method uses a bi-directional long short-term memory (FL-BiLSTM) network that is optimized by an augmented trickster coyote algorithm for feature selection and classification. The study focuses on overcoming the limitations of existing gait recognition systems that struggle with changes in walking direction. The paper evaluates the performance of the proposed FL-BiLSTM classifier method on a dataset of gait sequences with different emotions and compares it with existing methods. The results show that the proposed method achieves high accuracy, sensitivity, and specificity in emotion recognition from gait.