Static Hand Gesture Angle Recognition via Aggregated Channel Features (ACF) Detector

Abstract

Static hand gesture recognition is critical in the development of a system for human-computer interaction. Many human-computer interactions, such as human-robot interaction, game control, control of smart home devices, and others, use hand gestures as a fundamental and natural language of the body. The direction of rotation of static hand gestures is the subject of this research, and the focus is on six degrees of rotation (0°, 45°, 90°, 180°, 270°, and 315°). This work presents an ideal approach that can recognize the angle of hand gestures based on the Aggregate Channel Features (ACF) detector. This approach consists of three main stages: preprocessing (image labelling), computer training, and hand angle detection based on the ACF detector. The training process consists of 25 stages. The static hand gesture dataset contained 569 images (361 for training and 208 for testing). The average time cost to detect all hand gesture angles was 0.9445 seconds, and all hand angles were recognized with 100% accuracy. This is a strong indication that supports our approach.

Target Detection in Video Images Using HOG-Based Cascade Classifier

“…Object detection is one of the vital research topics as it used wide range of application such as intelligent surveillance [8,9], home automation [10], manufacturing [11], and healthcare [12]. There is other common application like face detection [13], character recognition [14], autonomous driving [15], hand gesture recognition [16], etc.…”