A Study on the Correlation Between Emotion and the Quality of Life of the Elderly Under the Artificial Intelligent Smile Recognition System: Protocol for a Descriptive Study

With the aging of the population, the senior people’s quality of life has become a hot issue in China and even in the world. This study, with emotion as the core, evaluated the senior citizens’ quality of life in the form of scales to explore the relationship between emotion and human health. Artificial intelligent identification system has developed rapidly in this society and has made great contributions to the development of human society. Combining artificial intelligent identification system with medicine will contribute to the development of human public health, which is of great significance and value.

Are paired or single stimuli better to recognize genuine and posed smiles from observers' GSR

<div>Smile recognition plays a vital role in human-human and human-computer interactions. This paper demonstrates a system to recognize the genuine and posed smiles by sensing observers’ galvanic skin response (GSR), while watching sets of images and videos. The smiles were shown either in ‘paired’ or in ‘single’ forms. Here, ‘paired’ means that the same smiler was seen in both genuine and posed</div><div>smile forms, otherwise the condition is referred to as ‘single’. The GSR signals were recorded and processed, and several time-domain and frequency-domain features were extracted from the processed GSR signals. Classification accuracies were found to be as high as 93.6% and 91.4% from paired and single conditions respectively. In comparison, observers were verbally 59.8% and 56.2% correct. Our results demonstrate that human subconscious responses (i.e. GSR signals) is better than their own verbal response, where the paired condition is slightly better than the single condition.</div>

Are paired or single stimuli better to recognize genuine and posed smiles from observers' GSR

<div>Smile recognition plays a vital role in human-human and human-computer interactions. This paper demonstrates a system to recognize the genuine and posed smiles by sensing observers’ galvanic skin response (GSR), while watching sets of images and videos. The smiles were shown either in ‘paired’ or in ‘single’ forms. Here, ‘paired’ means that the same smiler was seen in both genuine and posed</div><div>smile forms, otherwise the condition is referred to as ‘single’. The GSR signals were recorded and processed, and several time-domain and frequency-domain features were extracted from the processed GSR signals. Classification accuracies were found to be as high as 93.6% and 91.4% from paired and single conditions respectively. In comparison, observers were verbally 59.8% and 56.2% correct. Our results demonstrate that human subconscious responses (i.e. GSR signals) is better than their own verbal response, where the paired condition is slightly better than the single condition.</div>

Visual Focus of Attention and Spontaneous Smile Recognition Based on Continuous Head Pose Estimation by Cascaded Multi-Task Learning

Multi-person Visual focus of attention (M-VFOA) and spontaneous smile (SS) recognition are important for persons’ behavior understanding and analysis in class. Recently, promising results have been reported using special hardware in constrained environment. However, M-VFOA and SS remain challenging problems in natural and crowd classroom environment, e.g. various poses, occlusion, expressions, illumination and poor image quality, etc. In this study, a robust and un-invasive M-VFOA and SS recognition system has been developed based on continuous head pose estimation in the natural classroom. A novel cascaded multi-task Hough forest (CM-HF) combined with weighted Hough voting and multi-task learning is proposed for continuous head pose estimation, tip of the nose location and SS recognition, which improves accuracies of recognition and reduces the training time. Then, M-VFOA can be recognized based on estimated head poses, environmental cues and prior states in the natural classroom. Meanwhile, SS is classified using CM-HF with local cascaded mouth-eyes areas normalized by the estimated head poses. The method is rigorously evaluated for continuous head pose estimation, multi-person VFOA recognition, and SS recognition on some public available datasets and real-class video sequences. Experimental results show that our method reduces training time greatly and outperforms the state-of-the-art methods for both performance and robustness with an average accuracy of 83.5% on head pose estimation, 67.8% on M-VFOA recognition and 97.1% on SS recognition in challenging environments.

Interactive Art—Smiling Buddha: Recording the Moment at Which an Observer Smiles through Sight Detection and Smile Recognition

In the interactive installation Smiling Buddha, we aimed to “pass on” a smile from one observer to the next. Thus, we have designed a natural interactive process that keeps passing on smiles. The system captures the moment at which an observer smiles before kinetically recording the moment and saving the images. The system does not merely record an image from a single angle; instead, the device records the user’s smile from various angles during the interaction. The final smile features different angles of smiles from previous users together with the smile of the present user. After completing the interactive experience, the user’s data will be saved and transmitted to the “Smiling Database,” where the smiles of past users will then be reproduced in the display area. Through the vast quantity of smiles, we wish to achieve our core concept of “passing on a smile.”