Extraction of Subject-Specific Facial Expression Categories and Generation of Facial Expression Feature Space using Self-Mapping

Abstract

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char; mso-layout-grid-align: none;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">This paper proposes a generation method of a subject-specific Facial Expression Map (FEMap) using the Self-Organizing Maps (SOM) of unsupervised learning and Counter Propagation Networks (CPN) of supervised learning together. The proposed method consists of two steps. In the first step, the topological change of a face pattern in the expressional process of facial expression is learned hierarchically using the SOM of a narrow mapping space, and the number of subject-specific facial expression categories and the representative images of each category are extracted. Psychological significance based on the neutral and six basic emotions (anger, sadness, disgust, happiness, surprise, and fear) is assigned to each extracted category. In the latter step, the categories and the representative images described above are learned using the CPN of a large mapping space, and a category map that expresses the topological characteristics of facial expression is generated. This paper defines this category map as an FEMap. Experimental results for six subjects show that the proposed method can generate a subject-specific FEMap based on the topological characteristics of facial expression appearing on face images.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p

Basic research on facial expression recognition model with adaptive learning capability

“…Earlier reports [6] [7] have presented a generation method of a subject-specific emotional feature space using the SelfOrganizing Maps (SOM) [8] and the Counter Propagation Networks (CPN) [9]. The feature space expresses the correspondence relationship between the change of facial expression pattern and the strength of emotion on the twodimensional space centering on "pleasantness" and "arousal".…”

Basic Study on Facial Expression Recognition Model Adaptable along Time-axis

“…Previously, we presented a generation method for a subject-specific emotional feature space using Counter Propagation Networks (CPNs) [1]. Practically speaking, we created two kinds of feature space, a Facial Expression Map and an Emotion Map, by learning the facial images using a CPN.…”

Quantification of Emotions for Facial Expression: Generation of Emotional Feature Space Using Self-Mapping

“…A method for generating a subject-specific emotional feature space using self-organizing maps (SOMs) [12] and counter propagation networks (CPNs) [13] has been proposed in previous studies [10,11]. The feature space expresses the correspondence between the changes in facial expression patterns and the degree of emotions in a two-dimensional space centered on "pleasantness" and "arousal."…”

“…Previously, we proposed a method for generating a subject-specific feature space to estimate the grade of emotion, i.e., an emotional feature space that expresses the correspondence between physical and psychological parameters [10,11]. In this chapter, we improve the abovementioned method.…”