3D geometry-based face recognition in presence of eye and mouth occlusions

Dagnes, Nicole; Marcolin, Federica; Nonis, Francesca; Tornincasa, Stefano; Vezzetti, Enrico

doi:10.1007/s12008-019-00582-7

Cited by 17 publications

(9 citation statements)

References 31 publications

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“…Likewise, Ruiqin et al [ 39 ] introduced a face recognition access entrance guard system. Dagnes et al [ 40 ] investigated face recognition with eye and mouth occlusions in 3D geometry.…”

Section: Related Workmentioning

confidence: 99%

Masked Face Analysis via Multi-Task Deep Learning

Patel

Nie

et al. 2021

J. Imaging

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Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

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“…Likewise, Ruiqin et al [ 39 ] introduced a face recognition access entrance guard system. Dagnes et al [ 40 ] investigated face recognition with eye and mouth occlusions in 3D geometry.…”

Section: Related Workmentioning

confidence: 99%

Masked Face Analysis via Multi-Task Deep Learning

Patel

Nie

et al. 2021

J. Imaging

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“…Dagnes et al [26] proposed a method for 3D face recognition, robust to the eye and mouth occlusions. These obstructions were detected and removed by exploiting the 3D geometry, i.e., by considering their effects on the 3D points.…”

Section: Face Recognition Against Occlusionmentioning

confidence: 99%

State of the Art: Face Recognition

Biswas¹,

Blanco‐Medina²

2021

Preprint

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Working with Child Sexual Exploitation Material (CSEM) in forensic applications might be benefited from the progress in automatic face recognition. However, discriminative parts of a face in CSEM, i.e., mostly the eyes, could be often occluded to difficult the victim's identification. Most of the face recognition approaches cannot deal with such kind of occlusions, resulting in inaccurate face recognition results. This document presents a short review face recognition methods for images with natural and eye occlude faces. The purpose is to select the best baseline approach for solving automatic face recognition of occluded faces.

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“…Figure 1 shows an example of a facial depth map acquired by the sensor, the corresponding facial shell, and a 3D face image from Bosphorus. Geometrical descriptors k1 and C are computed on this data to obtain 3D Face Descriptors [31,33,34], used as input channels in addiction to facial depth maps. Then, as the last step to adapt the data to the input dimension of MobileNetV2, all the channels need to be resized to 224 × 224.…”

Section: Data Preparation and Pre-processingmentioning

confidence: 99%

Questionnaires or Inner Feelings: Who Measures the Engagement Better?

et al. 2020

Self Cite

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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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3D geometry-based face recognition in presence of eye and mouth occlusions

Cited by 17 publications

References 31 publications

Masked Face Analysis via Multi-Task Deep Learning

Masked Face Analysis via Multi-Task Deep Learning

State of the Art: Face Recognition

Questionnaires or Inner Feelings: Who Measures the Engagement Better?

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