Robust 3D Face Recognition in Presence of Pose and Partial Occlusions or Missing Parts

Bagchi, Parama; Bhattacharjee, Debotosh; Nasipuri, Mita

doi:10.5121/ijfcst.2014.4402

Cited by 24 publications

(7 citation statements)

References 9 publications

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“…Restoration approach: Here, the occluded regions in the probe faces are restored according to the gallery ones. For instance, Bagchi et al [8] proposed to restore facial occlusions. The detection of the occluded regions is carried out by thresholding the depth map values of the 3D image.…”

Section: Related Workmentioning

confidence: 99%

Efficient Masked Face Recognition Method during the COVID-19 Pandemic

Hariri

2020

Preprint

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The COVID-19 is an unparalleled crisis leading to huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes the face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on discard masked region and deep learning based features in order to address the problem of masked face recognition process. The first step is to discard the masked face region. Next, we apply a pre-trained deep Convolutional neural networks (CNN) to extract the best features from the obtained regions (mostly eyes and forehead regions). Finally, the Bag-of-features paradigm is applied on the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance.

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Section: Related Workmentioning

confidence: 99%

Efficient Masked Face Recognition Method during the COVID-19 Pandemic

Hariri

2020

Preprint

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“…In [110], Bagchi et al used ICP to register a 3D range image, and PCA to restore the occluded region. This method is robust to the noise and occlusions.…”

Section: B Local Feature-based Methodsmentioning

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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“…A system to recognize partially occluded faces with different poses was developed by Bagchi et al [14]. Weighted median filters were applied to the dataset to remove noise.…”

Section: Literature Reviewmentioning

confidence: 99%

Is Face Recognition with Masks Possible?

Saib¹,

Pudaruth²

2021

IJACSA

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With the recent outbreak of the COVID-19 pandemic, wearing face masks has become extremely important to protect us, and to reduce the spread of the virus. This measure has made many existing face recognition systems ineffective as they were trained to work with unmasked faces. In this paper, several methods have been proposed for masked face recognition. Two pre-trained deep learning architectures (VGG16, and MobileNetV2) and the Histogram of Gradients (HOG) technique were used to extract the relevant features from face images of celebrities. A SoftMax layer and Support Vector Machines (SVM) were used for classification. Five scenarios were devised to assess the different models and approaches. With an accuracy of 96.8%, the best model was obtained with MobileNetV2 with a SoftMax layer on the dataset consisting of a mixture of masked and unmasked images. Three different types of masks were also used in this study. The mean accuracy was 91.35% when the same type of mask is used for training and testing. However, the accuracy dropped by an average of 5.6% when a different type of mask is used for training and testing. A contactless attendance system using the best masked face recognition model has also been implemented.

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Robust 3D Face Recognition in Presence of Pose and Partial Occlusions or Missing Parts

Cited by 24 publications

References 9 publications

Efficient Masked Face Recognition Method during the COVID-19 Pandemic

Efficient Masked Face Recognition Method during the COVID-19 Pandemic

3D Face Recognition: A Survey

Is Face Recognition with Masks Possible?

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