3D Face Recognition on Point Cloud Data - An Approaching based on Curvature Map Projection using Low Resolution Devices

Nunes, Luis Felipe de Melo; Zaghetto, Caue; Vidal, Flávio de Barros

doi:10.5220/0006843702660273

Cited by 3 publications

(2 citation statements)

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“…Instead of triangulation, low-cost active sensors typically use a structured light to compute depth, which provides much faster but less accurate and more noisy measurements [ 34 ]. Face recognition methods in [ 35 , 36 ] that use low-cost sensors such as Kinect pay special attention to removing noise from images. These methods often rely on representing the face through local features that are not affected by regional noise and distortions due to missing data, which are characteristic of low-cost sensors.…”

Section: Related Workmentioning

confidence: 99%

Making the Most of Single Sensor Information: A Novel Fusion Approach for 3D Face Recognition Using Region Covariance Descriptors and Gaussian Mixture Models

Krizaj

Dobrišek

2022

Sensors

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Most commercially successful face recognition systems combine information from multiple sensors (2D and 3D, visible light and infrared, etc.) to achieve reliable recognition in various environments. When only a single sensor is available, the robustness as well as efficacy of the recognition process suffer. In this paper, we focus on face recognition using images captured by a single 3D sensor and propose a method based on the use of region covariance matrixes and Gaussian mixture models (GMMs). All steps of the proposed framework are automated, and no metadata, such as pre-annotated eye, nose, or mouth positions is required, while only a very simple clustering-based face detection is performed. The framework computes a set of region covariance descriptors from local regions of different face image representations and then uses the unscented transform to derive low-dimensional feature vectors, which are finally modeled by GMMs. In the last step, a support vector machine classification scheme is used to make a decision about the identity of the input 3D facial image. The proposed framework has several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrixes), the ability to explore facial images at different levels of locality, and the ability to integrate a domain-specific prior knowledge into the modeling procedure. Several normalization techniques are incorporated into the proposed framework to further improve performance. Extensive experiments are performed on three prominent databases (FRGC v2, CASIA, and UMB-DB) yielding competitive results.

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Section: Related Workmentioning

confidence: 99%

Making the Most of Single Sensor Information: A Novel Fusion Approach for 3D Face Recognition Using Region Covariance Descriptors and Gaussian Mixture Models

Krizaj

Dobrišek

2022

Sensors

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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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Estimating sex and age from a face: a forensic approach using machine learning based on photo-anthropometric indexes of the Brazilian population

et al. 2020

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3D Face Recognition on Point Cloud Data - An Approaching based on Curvature Map Projection using Low Resolution Devices

Cited by 3 publications

References 0 publications

Making the Most of Single Sensor Information: A Novel Fusion Approach for 3D Face Recognition Using Region Covariance Descriptors and Gaussian Mixture Models

Making the Most of Single Sensor Information: A Novel Fusion Approach for 3D Face Recognition Using Region Covariance Descriptors and Gaussian Mixture Models

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

Estimating sex and age from a face: a forensic approach using machine learning based on photo-anthropometric indexes of the Brazilian population

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