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

Abstract

<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>

Three-dimensional Face Recognition Method Based on Multiresolution Model and Fuzzy Random Matrix

“…Luuk et al divided the face into more than one overlapping area and fused these areas the use of predominant aspect evaluation linear discriminant analysis [13]. Using 60 fusion place classifiers, the attention fee is improved, and the correlation of adjoining wavelet coefficients is used to overcome the illumination change, which is used to understand 3D face recognition [14].…”

Video-based emotion sensing and recognition using convolutional neural network based kinetic gas molecule optimization

“…As we know, people identify emotion by combining difficult multimodal information and tend only to pay attention to significant information in various ways. For example, some people always talk while they keep a smile, while others talk loudly but not angrily [7]. Consequently, we deliberate those human beings do not detect emotions based on modal alignment.…”

“…The results of several emotion recognition simulations are provided, along with their performance factors. [7]. The extraction of representative modal features using profound learning technology has grown easier.…”

A Photometric Stereo Using Re-Projected Images for Active Stereo Vision System

“…Optical 3D measurement technology is used in many applications, such as reverse engineering, quality inspection, restoration of cultural properties, face recognition, and dental computer aided design/computer aided manufacturing (CAD/CAM), because of its high measurement speed and precision [1][2][3][4][5]. Many passive stereo vision technologies have been proposed [6,7], but active stereo vision using structured light is one of the most popular optical 3D measurement techniques.…”