Assessing Discriminating Capability of Geometrical Descriptors for 3D Face Recognition by Using the GH-EXIN Neural Network

Ciravegna, Gabriele; Cirrincione, Giansalvo; Marcolin, Federica; Barbiero, Pietro; Dagnes, Nicole; Piccolo, Elio

doi:10.1007/978-981-13-8950-4_21

Cited by 8 publications

(9 citation statements)

References 8 publications

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“…Geometrical descriptors were computed on the facial shells, as they are discriminative features to perform facial analysis [22]. 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].…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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Due to its versatility, virtual technology is being widely employed in different domains, from industry to amusement. The possibility to adopt this technology in early product/service design is going to bring positive effects such as the reduction of costs associated with the production of physical prototypes and the generation of a more effective knowledge of users’ feedback. This study proposes a preliminary methodology to evaluate users’ engagement in interacting with a virtual environment that consists of the integration between a self-report method (the user engagement scale questionnaire) and a method based on facial expression recognition. Results reported in this paper show that the two methodologies generate different types of knowledge which can be used to fit users’ needs and expectations. Even if focused on a specific case study, i.e., the evaluation of the engagement in a virtual learning environment, this paper aims to propose a methodology that can be applied to all kinds of virtual products.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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“…The selection of geometric feature descriptors is based on the GH-EXIN network. The reliability of geometric descriptors based on curvature is proven in [131]. The input data is a three-channel image including the 3D facial depth map, the shape index and the curvedness, which can enhance the accuracy of the network.…”

Section: B 3d Face Recognitionmentioning

confidence: 99%

3D Face Recognition: A Survey

Jing,

Lu,

Gao

2021

Preprint

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Face recognition is one of the most studied research topics in the community. In recent years, the research on face recognition has shifted to using 3D facial surfaces, as more discriminating features can be represented by the 3D geometric information. This survey focuses on reviewing the 3D face recognition techniques developed in the past ten years which are generally categorized into conventional methods and deep learning methods. The categorized techniques are evaluated using detailed descriptions of the representative works. The advantages and disadvantages of the techniques are summarized in terms of accuracy, complexity and robustness to face variation (expression, pose and occlusions, etc). The main contribution of this survey is that it comprehensively covers both conventional methods and deep learning methods on 3D face recognition. In addition, a review of available 3D face databases is provided, along with the discussion of future research challenges and directions.

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“…The default input size for MobileNetV2 is 224 × 224 with 3 channels, so a preprocessing step is required to adapt the data to the neural network input. Similarly to a preceding work [30], geometrical descriptors [31] have been computed from 3D depth maps, retrieved both from Bosphorus DB and from the sensor, to interpret RGB (red, green, blue) images with a novel representation. In this study, based on previous results [32], the first channel is the facial 3D depth map, the second channel is the first principal curvature (k1), and the third one is the curvedness (C).…”

Section: Data Preparation and Pre-processingmentioning

confidence: 99%

Questionnaires or Inner Feelings: Who Measures the Engagement Better?

et al. 2020

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This work proposes an innovative method for evaluating users’ engagement, combining the User Engagement Scale (UES) questionnaire and a facial expression recognition (FER) system, active research topics of increasing interest in the human–computer interaction domain (HCI). The subject of the study is a 3D simulator that reproduces a virtual FabLab in which users can approach and learn 3D modeling software and 3D printing. During the interaction with the virtual environment, a structured-light camera acquires the face of the participant in real-time, to catch its spontaneous reactions and compare them with the answers to the UES closed-ended questions. FER methods allow overcoming some intrinsic limits in the adoption of questioning methods, such as the non-sincerity of the interviewees and the lack of correspondence with facial expressions and body language. A convolutional neural network (CNN) has been trained on the Bosphorus database (DB) to perform expression recognition and the classification of the video frames in three classes of engagement (deactivation, average activation, and activation) according to the model of emotion developed by Russell. The results show that the two methodologies can be integrated to evaluate user engagement, to combine weighted answers and spontaneous reactions and to increase knowledge for the design of the new product or service.

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Assessing Discriminating Capability of Geometrical Descriptors for 3D Face Recognition by Using the GH-EXIN Neural Network

Cited by 8 publications

References 8 publications

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

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

3D Face Recognition: A Survey

Questionnaires or Inner Feelings: Who Measures the Engagement Better?

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