# Correlation between Human Emotion and Temporal-Spatial Contexts by Analyzing Environmental Factors
## 2 Literature Review  
The first part of the literature review focuses on image recognition research based on temporal and spatial contexts. Many previous studies have emphasized the analysis of scene recognition [8; 9; 10; 11]. [PERSON] et al. studied object and scene recognition by evaluating color descriptors such as color histograms and color SIFT descriptors [16]. In [17], scene recognition research was performed using a place-labeled database according to the development of deep learning, such as convolutional neural networks (CNNs). [PERSON] and [PERSON] performed scene recognition using the chromatic color space and spatial scales [18]. In addition, computational auditory scene recognition has been performed using the confusion matrix for 17 scenes classified using the MFCC and GMM methods in the time and frequency domains [19]. However, although many studies have been performed on scene recognition using many different methods, the aforementioned research problem has not yet been widely investigated. In particular, the effect of specific environmental factors on human emotions has not been widely researched.  
The second part of the literature review focuses on the study of human emotion. For several years, this relationship with emotion has been widely investigated based on consciously affective experiences in terms of human emotion [20]. [PERSON] et al. studied natural human emotions using face recognition and Markov random field models [21]. [PERSON] et al. studied the multimodal method for recognition of human emotion and expression by analyzing speech and human facial expressions based solely on images [22]. Inaddition, [PERSON] et al. investigated emotion recognition by analyzing physiological signals such as EMG, RV, SKT, SKC, BVP and HR [23].  
The remainder of the literature review is the basis for the study of the association between human emotion models and color. According to the emotion model of [PERSON], human emotion can be defined as an emotion model that is two-dimensional for the construction of various emotions such as the \"Pleasant-Unpleasant\" axis (X) and \"Aroused-Not Aroused\" axis (Y), as shown in Figure 1[20]. Based on the emotion model of [PERSON], many studies have researched the relationship between color-based mapping for various emotional states and human emotion. Generally, visualized information can indicate the correlation between color and human emotion in the \"Geneva Emotion Wheel [24]\". In this case, human emotion is mapped into specific colors by dividing it into two dimensions: \"Pleasant-Unpleasant\" (X-axis) and \"Arousal-Relaxation\" (Y-axis), as indicated in Figure 2.  
We confirmed that these methods are used to analyze human emotions such as color components and the visual content of the surrounding environment [20]. Based on these theories, we propose a method for analyzing the correlation between human emotion and temporal-spatial factors using a camera to capture the front environment and quantitatively extract visual context information from the acquired images. In the first step, the surrounding environment is photographed using the camera of a mobile device for image acquisition. Second, a subjective evaluation of the user is performed based on the acquired video for subsequent utilization as an index of the user's emotional information. Third, a total of nine types of features are extracted from the acquired video: temporal complexity (#1), spatial complexity (horizontal edge (#2), vertical edge (#3)), color components (hue  
Figure 1: [PERSON]’s human emotion model [20].  
Figure 2: Geneva emotion wheel model [24].  
(#4), saturation (#5), intensity (#6), contrast (#7)), and sounds (amplitude (#8), frequency (#9)) to analyze the impact of each factor on human emotion. Finally, emotion estimation technology was developed by designing and analyzing a fully connected support vector regression (SVR) inference network based on the nine types of factors and the results of the subjective evaluation of the user.