Computer Vision for Facial Analysis Using Human-Computer Interaction Models

A facial recognition device is a technology that can be used for authenticating a human face by ID verification and for calculating facial features from a single image using a visual image or a video to a facial database. Computer vision's main objective enables identifying images, detecting speech, the concentration of attention, facial expressions, and recognition of emotions. Face identification is an important part of the understanding of audiovisual speech. In this paper, Intelligent Schemes for human-computer interaction (IS-HCI) provides a broader and more expressive range of computer-based vision features for processing data from one or more sensors. In particular cases of impairment, alternative interaction methods dependent on computer vision can be effectively modified. Facial identification is a biometric safety category. This paper aims to help technology-based concepts, allowing a child with a debilitating neuromuscular disorder to communicate with the robot by recognizing facial expressions. The proposed model was suggested to test in photographs taken from children's videos, and the preliminary findings show that computational interactions with children through facial expression would break down barriers to their relationship with machines with people with limited mobility. Thus the experimental results show the proposed method IS-HCI to enhance facial expression and effectively interact with the children to achieve sensitivity (94.12%), specificity (96.84%), recognition rate (94.23%), probability (21.78%), accuracy (96.69%), expression prediction rate (98.14%), muscle activity level (79.84%) compared to other methods.