Multimodal Fingerprint Spoof Detection Using White Light

Balaji, A

doi:10.1016/j.procs.2016.02.066

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…In comparison to traditional methods such as PIN, secret password, finger impression (Multimodal fingerprint spoof detection using white light 2016 ; Schroff et al 2015 ), etc., face recognition has gained a lot of attention as a remarkable biometric authentication procedure across all automatic personal authentication systems (Singh et al 2021 ) (Hong et al 2021 ; Nefian and Hayes 2000 ). Many organizations and government bodies rely on this technique to protect their assets and to secure public places such as airports, bus stands, and railway stations etc.…”

Section: Introductionmentioning

confidence: 99%

VGG16-random fourier hybrid model for masked face recognition

Sikha

Bharath

2022

Soft Comput

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With the recent COVID-19 pandemic, wearing masks has become a necessity in our daily lives. People are encouraged to wear masks to protect themselves from the outside world and thus from infection with COVID-19. The presence of masks raised serious concerns about the accuracy of existing facial recognition systems since most of the facial features are obscured by the mask. To address these challenges, a new method for masked face recognition is proposed that combines a cropping-based approach (upper half of the face) with an improved VGG-16 architecture. The finest features from the un-occluded facial region are extracted using a transfer learned VGG-16 model (Forehead and eyes). The optimal cropping ratio is investigated to give an enhanced feature representation for recognition. To avoid the overhead of bias, the obtained feature vector is mapped into a lower-dimensional feature representation using a Random Fourier Feature extraction module. Comprehensive experiments on the Georgia Tech Face Dataset, Head Pose Image Dataset, and Face Dataset by Robotics Lab show that the proposed approach outperforms other state-of-the-art approaches for masked face recognition.

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Section: Introductionmentioning

confidence: 99%