Solving the face growth problem in the biometrie face recognition using Photo-Anthropometric ratios by iris normalization

Bicalho, Gustavo Carneiro; Alves, Marcelo Cobias Amorim; Porto, Lucas Faria; Machado, Carlos Eduardo Palhares; Vidal, Flávio de Barros

doi:10.1109/iwbf.2018.8401553

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…There is a strong interest in improving the accuracy and robustness of current identification techniques to maximize their quality and value as court evidence. As a result, various efforts have been made to address different sources of uncertainty involved in the procedure from an anatomical point of view (Bicalho et al, 2018;Campomanes-Álvarez et al, 2018). However, it is important to highlight that knowledge of the scene context and acquisition parameters of the camera is essential prior to the application of any anatomical analysis.…”

Section: Introductionmentioning

confidence: 99%

FacialSCDnet: A deep learning approach for the estimation of subject-to-camera distance in facial photographs

Bermejo

Fernández-Blanco

Valsecchi

et al. 2022

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

FacialSCDnet: A deep learning approach for the estimation of subject-to-camera distance in facial photographs

Bermejo

Fernández-Blanco

Valsecchi

et al. 2022

Expert Systems with Applications

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“…The research on automatically identifying and extracting facial features from images, is not a recent scientific field and we easily can found studies from the 80's. However, automatic landmark location in particular have been increasingly attracting the interest of researchers due to its multiple application in fields such as: identification and facial recognition [14,15,16], facial modeling using 3D images [17,18], tracking [19], cephalometric points identification [20], sex and age estimation [21,22] and so on. Several studies have been proposed for automatic identification of facial points.…”

Section: Introductionmentioning

confidence: 99%

Automatic cephalometric landmarks detection on frontal faces: An approach based on supervised learning techniques

Porto

Lima

Flores

et al. 2019

Digital Investigation

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Facial landmarks are employed in many research areas such as facial recognition, craniofacial identification, age and sex estimation among the most important.In the forensic field, the focus is on the analysis of a particular set of facial landmarks, defined as cephalometric landmarks. Previous works demonstrated that the descriptive adequacy of these anatomical references for an indirect application (photo-anthropometric description) increased the marking precision of these points, contributing to a greater reliability of these analyzes. However, most of them are performed manually and all of them are subjectivity inherent to the expert examiners. In this sense, the purpose of this work is the development and validation of automatic techniques to detect cephalometric landmarks from digital images of frontal faces in forensic field. The presented approach uses a combination of computer vision and image processing techniques within a supervised learning procedures. The proposed methodology obtains similar precision to a group of human manual cephalometric reference markers and result to be more accurate against others state-of-the-art facial landmark detection frameworks. It achieves a normalized mean distance (in pixel) error of 0.014, similar to the mean inter-expert dispersion (0.009) and clearly better than other automatic approaches also analyzed along of this work (0.026 and 0.101).

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Solving the face growth problem in the biometrie face recognition using Photo-Anthropometric ratios by iris normalization

Cited by 2 publications

References 15 publications

FacialSCDnet: A deep learning approach for the estimation of subject-to-camera distance in facial photographs

FacialSCDnet: A deep learning approach for the estimation of subject-to-camera distance in facial photographs

Automatic cephalometric landmarks detection on frontal faces: An approach based on supervised learning techniques

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