Multidimensional nearest neighbors classification based system for incomplete lip print identification

Doroz, Rafał; Wróbel, Krzysztof; Orczyk, Tomasz; Porwik, Piotr; Cholewa, Marcin

doi:10.1016/j.eswa.2022.117137

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…However, at the moment, efforts are more devoted to lip images as a visible external attribute than on marks or prints left on surfaces. One research group in Poland published regularly on the lip print biometrics with regularly improved performance [ 487 ]. They reported an accuracy above 94% on a database of 1001 lip prints taken from 143 individuals (7 lip prints per person) with a technique allowing to detect low quality areas in the prints.…”

Section: Other Body Marks Case Reports and Miscellaneousmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions (2019 – 2022)

Bécue

Champod

2023

Forensic Science International: Synergy

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Section: Other Body Marks Case Reports and Miscellaneousmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions (2019 – 2022)

Bécue

Champod

2023

Forensic Science International: Synergy

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“…Niu et al [8] proposed a novel LBP based on bit plane for lip print feature extracting. Doroz et al [9] used multidimensional nearest neighbors classifier to recognize incomplete lip prints. Due to outstanding performance in the field of image recognition, neural networks (NN) and deep learning (DL) techniques are introduced in lip print recognition.…”

Section: Introductionmentioning

confidence: 99%

Lip print recognition based on convolutional spiking neural network

Niu¹,

Wang²,

Wu³

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Biological features are widely used for person identification, lip prints have been proved to be a unique and permanent part of the body. Therefore, lip print recognition can be considered as an effective mean to confirm individuals. This paper developed an architecture with convolutional spiking neural network (CSNN) for lip print recognition. Spiking neural networks (SNN) have the potential to reduce power consumption comparing to traditional artificial neural networks (ANN). The leaky-integrate-and-fire (LIF) neuron model and gradient surrogate method were used in the proposed CSNN. This work provided a novel strategy for lip print recognition.

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