Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

Travieso, Carlos M.; Zhang, Jianguo; Miller, Paul; Alonso, Jesús B.

doi:10.1016/j.imavis.2014.10.001

Cited by 15 publications

(3 citation statements)

References 37 publications

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“…In 2014, Travieso et al. [21] developed a lip biometric approach based on shape information using a Discrete Hidden Markov Model (DHMMK). The lips are described by shape features (geometrical and sequential), which are then modelled by a DHMMK and classified using an SVM.…”

Section: Literature Studymentioning

confidence: 99%

Lip print‐based identification using traditional and deep learning

Farrukh

Haar

2022

IET Biometrics

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The concept of biometric identification is centred around the theory that every individual is unique and has distinct characteristics. Various metrics such as fingerprint, face, iris, or retina are adopted for this purpose. Nonetheless, new alternatives are needed to establish the identity of individuals on occasions where the above techniques are unavailable. One emerging method of human recognition is lip-based identification. It can be treated as a new kind of biometric measure. The patterns found on the human lip are permanent unless subjected to alternations or trauma. Therefore, lip prints can serve the purpose of confirming an individual's identity. The main objective of this work is to design experiments using computer vision methods that can recognise an individual solely based on their lip prints. This article compares traditional and deep learning computer vision methods and how they perform on a common dataset for lip-based identification. The first pipeline is a traditional method with Speeded Up Robust Features with either an SVM or K-NN machine learning classifier, which achieved an accuracy of 95.45% and 94.31%, respectively. A second pipeline compares the performance of the VGG16 and VGG19 deep learning architectures. This approach obtained an accuracy of 91.53% and 93.22%, respectively.

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Section: Literature Studymentioning

confidence: 99%

Lip print‐based identification using traditional and deep learning

Farrukh

Haar

2022

IET Biometrics

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“…In the analysis of mammographic images with noise, a method was proposed for estimating the fractal dimension in 3D, modelling it as a fractional brownian motion and using the Spectral Power Density of Fourier and its correlation with the roughness. It has been found that an increase in the noise of the image increases its fractal dimension [36,37]. In our study, we will use the counting method of boxes (box-counting), taking into account some of the recommendations of the method to avoid excessive variations:…”

Section: Calculation Of the Fractal Dimensionmentioning

confidence: 99%

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Landeros¹,

Meneses²,

Rodríguez³

et al. 2018

CyS

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A set of images of metal pieces with different finishes was obtained for use as a standard reference in the estimate of the surface roughness (Ra) through the fractal dimension (D) of the power spectrum and the spectrum of intensities. A system of artificial vision with two sources of LED light illumination (white and red) and three angles of incidence was used. The best results were found with the spectra of intensities regardless of the type of lighting or the angle of incidence. The values of the fractal dimension were correlated with the surface roughness values obtained with a contact profilometer to build regression curves that are used to estimate the surface roughness on site with a statistical error under 5%. This system could be used as an inspection station to reduce waiting times and unnecessary transport (Poka Yoke System).

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“…In general, features can be classified into texture, shape, and color. The form and position of the lip furrows were analyzed in texture features such as Markov models [16], radon transform [17], Bifurcation's analysis [18], HT [19], Top_Hat [20], gabor filter, local binary pattern (LBP) [21], dynamic time warping (DTW) [22], [23], statical analysis [24], [25], scale invariant feature transform (SIFT), speeded up robust features (SURF) [26], location and inclination of the furrows [27], LBP, area and perimeter [28] and principal component analysis (PCA) [29]. Shape features include general geometric properties of the lips [30], Rotation, scale, and translation invariant image [31], and Shape descriptors [32].…”

Section: Introductionmentioning

confidence: 99%

Personal identification using lip print furrows

Anai

Mersal

Mostafa

2023

IJEECS

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There are common techniques for personal identification such as fingerprint recognition, face recognition, and iris recognition. In this paper, we suggest a new method for personal identification using lip print furrows. This method will be useful in forensics services. This study aims to identify people by their lip print patterns that are unique for each person. Lip print features are extracted from the lip print images by using the vertical Sobel operator, diagonal ±45 lines, and hough transform. A lip print pattern was formed for each person containing only the furrows (the extracted features) that were extracted from 3 lip print images for that person. The correlation coefficients and the mean squared error (MSE) are provided and passed to the support vector machine (SVM) for the classification process. The suggested method gives good results. A comparison between the results of the proposed method and other methods was presented.

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Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

Cited by 15 publications

References 37 publications

Lip print‐based identification using traditional and deep learning

Lip print‐based identification using traditional and deep learning

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Personal identification using lip print furrows

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