3D Facial Expression Recognition for Defining Users’ Inner Requirements—An Emotional Design Case Study

Abstract

This study proposes a novel quality function deployment (QFD) design methodology based on customers’ emotions conveyed by facial expressions. The current advances in pattern recognition related to face recognition techniques have fostered the cross-fertilization and pollination between this context and other fields, such as product design and human-computer interaction. In particular, the current technologies for monitoring human emotions have supported the birth of advanced emotional design techniques, whose main focus is to convey users’ emotional feedback into the design of novel products. As quality functional deployment aims at transforming the voice of customers into engineering features of a product, it appears to be an appropriate and promising nest in which to embed users’ emotional feedback with new emotional design methodologies, such as facial expression recognition. This way, the present methodology consists in interviewing the user and acquiring his/her face with a depth camera (allowing three-dimensional (3D) data), clustering the face information into different emotions with a support vector machine classificator, and assigning customers’ needs weights relying on the detected facial expressions. The proposed method has been applied to a case study in the context of agriculture and validated by a consortium. The approach appears sound and capable of collecting the unconscious feedback of the interviewee.

Three-Dimensional Evaluation of Soft Tissue Malar Modifications after Zygomatic Valgization Osteotomy via Geometrical Descriptors

“…Differential geometry descriptors have shown their versatility and applicability in different fields, recently being applied in very different contexts: the first one related to face analysis applications [ 35 ], the second in the field of protein biophysics [ 36 ]. In Figure 4 , the original facial surface and the three considered geometrical descriptors are shown.…”

Engagement Evaluation in a Virtual Learning Environment via Facial Expression Recognition and Self-Reports: A Preliminary Approach

“…Previous articles assessed the reliability of facial analysis performed using the implementation of geometrical descriptors in a face recognition application [23] and the discriminative capability of these features with a neural network approach [24]. To classify the data in the three classes of engagement, an SVM (support vector machine) classification method implemented in python was used, relying on that proposed in a previous work by Violante et al for defining the inner users' requirements [25]. Since our purpose was to compare the result given by the classification based on facial analysis and the result shown by the questionnaire, values of Likert scale used in the UES were grouped into three classes.…”

Detection of Emotion Using Multi-Block Deep Learning in a Self-Management Interview App

“…The dataset used in the early stages was secured in a limited environment; however, videos that contain everyday situations are in the dataset [22]. Figure 3 shows how to recognize a face in a three-dimensional (3D) image [23,24]. The flow of the method can be separated from the training phase and the test phase.…”