A novel periocular biometrics solution for authentication during Covid-19 pandemic situation

Kumari, Punam; Seeja, K. R.

doi:10.1007/s12652-020-02814-1

Cited by 41 publications

(17 citation statements)

References 37 publications

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“…Table 5 presents a comparison of this proposal with the other works. Other proposals in face detection have also reviewed, but with the ocular recognition variant, as seen in [52,53]. However, it should be clarified that focusing on the human eye represents a different approach than the one analyzed in this document.…”

Section: Discussionmentioning

confidence: 99%

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Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

et al. 2021

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In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.

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Section: Discussionmentioning

confidence: 99%

“…The results present an accuracy of between 90-95%. Similarly, [53] provides a facial recognition system using SVM with three databases (UBIPr, Color FERET, and Ethnic Ocular). Performance tests show a yield of approximately 92%.…”

Section: Introductionmentioning

confidence: 99%

Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

et al. 2021

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show abstract

“…On the other hand, some works propose feature fusion approach which combines handcrafted features (e.g. LBP and HOG) with features extracted using pretrained CNN models [18], [36]. Another hybrid model is introduced in [1] for ocular smartphone authentication (Selfie Biometrics).…”

Section: B Periocular Face Recognitionmentioning

confidence: 99%

“…In the case of periocular face datasets, we use the bi-ocular information including, in a single image, both eyes and considering the eyelids, eyelashes, eyebrow, tear duct, eye shape and the surrounding skin. For obtaining this periocular region, we crop face images based on the algorithm used in [18] for extracting the region of interest. This algorithm considers the canthus points as reference points, which were detected automatically through Dlib landmarks detector.…”

Section: Periocular Datasetsmentioning

confidence: 99%

“…tends to degrade by a large margin in front of specific objects like the COVID-19 masks, that occlude a large part of the face, including important facial regions such as the mouth and the nose [29]. This is the reason why recognizing masked faces is currently an active research topic [7], [18], [37]. In the last year, recent works [4], [8], [29] have evaluated the effect of wearing a mask on automatic face recognition systems based on state-of-the-art deep Convolution Neural Networks (CNN).…”

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confidence: 99%

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