Uncertainty of 3D facial features measurements and its effects on personal identification

Zappa, Emanuele; Testa, Riccardo; Barbesta, Marco; Gasparetto, M.

doi:10.1016/j.measurement.2013.11.055

Cited by 9 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, as the number of facial images used for the training increases, the AAM model is able to cover a broader range of shape and texture variations. However, when the number of images used for training grows, the handling of such a great size of information becomes hard, the weight of random effects over deterministic phenomena becomes higher and the final reliability of face recognition becomes lower [13]. A good compromise has been found building a model using 200 images from 50 people belonging to the database of 117 individuals.…”

Section: The Identity Databasementioning

confidence: 99%

“…Biometric features in 3D space AAM Triangulation the 3D coordinates is estimated ( is an estimate of identification uncertainty), the weight for the -th landmark is defined as the inverse of [13].…”

Section: Stereo Acquisition Segmentationmentioning

confidence: 99%

“…Therefore, it is not possible for those systems to deal with pose variation or the natural variation of face expression. Conversely, the authors proposed a system that provides a fusion between the point-cloud 3D reconstruction and the pure image feature analysis by using a stereoscopic vision rig [13], [14]. The stereoscopic setup acquires synchronously from several different calibrated cameras the images of a person's face, so that it is possible to triangulate the feature points and express the biometric characteristics in the 3D space using couples of cameras grabbing the face from different orientation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Metrological characterization of 3D biometric face recognition systems in actual operating conditions

et al. 2017

View full text Add to dashboard Cite

<p>Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few.<br />In such scenarios, the architectures based on stereo vision and 3D reconstruction of the face are going to assume a predominant role because they can generally assure a better reliability than solutions based on a single camera (which make use of a single image instead of a couple of images). To realize such systems, different architectures can be considered by varying the positioning of the pair of cameras with respect to the face of the subject to be identified, as well as both kind and resolution of camera considered. These parameters can affect the correct decision rate of the system in classifying the input face, especially in presence of image uncertainty.<br />In this paper, several 3D architectures differing in camera specifications and geometrical positioning of the camera pair (with respect to the input face) are realized and compared. The detection of facial features in the images is made by adopting a popular method based on the Active Appearance Model (AAM) algorithm. 3D position of facial features is then obtained by means of stereo triangulation. The performance of the realized systems has been compared in terms of sensitivity to the quantities of influence and related uncertainty, and of typical indexes for the analysis of classification systems. Main results of such comparison show that the best performance can be reached by reducing the distance between cameras and subject to be identified and by minimizing the horizontal angle between the plane containing the camera pair axis and the face to be identified.</p>

show abstract

Section: The Identity Databasementioning

confidence: 99%

Section: Stereo Acquisition Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Metrological characterization of 3D biometric face recognition systems in actual operating conditions

et al. 2017

View full text Add to dashboard Cite

show abstract

“…There are two methods, feature-based method and optical-flow method, which are used in solving these problems. Feature-based method [5][6][7] requires the relationship between a set of features extracted from one image and those extracted from the next image. There have been various features, including points, lines, planes, conics and combinations of these features.…”

Section: Introductionmentioning

confidence: 99%

A novel image analysis method for rotational motion of circular feature based on perspective projection

Liu

Zhao

2015

The Imaging Science Journal

View full text Add to dashboard Cite

Circular feature has been widely applied in recognising moving object and estimating its position and orientation. Based on the principle of perspective projection, the rotational motion of circular feature was analysed. According to whether the rotational motion occurred in the plane of circular feature, the rotational motion could be classified into two types: in-plane rotation and non-in-plane rotation. For the convenience of computation, we set the camera's initial position and orientation. The optical axis of camera was set perpendicular to the plane of circular feature. We analysed the perspective distortion of the circular feature. Additionally, based on the geometrical reasoning, we also proposed the method of computing the rotation angles. Finally, some experiments were carried to verify correctness and feasibility of the computing method. The results showed that the measurement of rotation angle could achieve better performance though the error existed. The maximum value of errors was only 0?4u. The average errors and the standard deviations of errors were also small.

show abstract

“…In this field, the authors tackled the problem of the metrological characterization of a face recognition classification system [3], [4], by proposing an original method for the evaluation of an uncertainty model in face recognition systems [11]- [13]. In particular, starting from the analysis of the acquired image, the model on-line estimates the uncertainty of the quantities employed in the classification stage.…”

Section: Introductionmentioning

confidence: 99%

A proposal for improving the performance of face recognition systems based on 3d features

Betta

Capriglione

Corvino

et al. 2015

2015 XVIII AISEM Annual Conference

Self Cite

View full text Add to dashboard Cite

In this paper a suitable methodology for the improvement of the reliability of results in classification systems based on 3D images is proposed. More in detail, it is based on the knowledge of the uncertainty of the features constituting the 3D image (obtained processing a pair of two 2D stereoscopic images) and on a suitable statistical approach providing a confidence level to the classification result. These pieces of information are then managed in order to improve the classification performance in terms of correct classification and missed classification percentages. The experimental results, obtained applying the methodology on an Active Appearance Models algorithm, a popular method for face recognition based on 3D features, show that, compared with a traditional approach (which generally does not take into account the uncertainty on 3D features), the proposed methodology allows to significantly improve the classification performance even in scenarios characterized by a high uncertainty.

show abstract

Uncertainty of 3D facial features measurements and its effects on personal identification

Cited by 9 publications

References 26 publications

Metrological characterization of 3D biometric face recognition systems in actual operating conditions

Metrological characterization of 3D biometric face recognition systems in actual operating conditions

A novel image analysis method for rotational motion of circular feature based on perspective projection

A proposal for improving the performance of face recognition systems based on 3d features

Contact Info

Product

Resources

About