Biometric Face Recognition Systems Research Articles

Savitzky-Golay filter energy features-based approach to face recognition using symbolic modeling.

Face recognition is a well-researched domain however many issues for instance expression changes, illumination variations, and presence of occlusion in the face images seriously affect the performance of such systems. A recent survey shows that COVID-19 will also have a considerable and long-term impact on biometric face recognition systems. The work has presented two novel Savitzky–Golay differentiator (SGD) and gradient-based Savitzky–Golay differentiator (GSGD) feature extraction techniques to elevate issues related to face recognition systems. The SGD and GSGD feature descriptors are able to extract discriminative information present in different parts of the face image. In this paper, an efficient and robust person identification using symbolic data modeling approach and similarity analysis measure is devised and employed for feature representation and classification tasks to address the aforementioned issues of face recognition. Extensive experiments and comparisons of the proposed descriptors experimental results indicated that the proposed approaches can achieve optimal performance of 96–97, 92–96, 100, 84–93, and 87–96% on LFW, ORL, AR, IJB-A datasets, and newly devised VISA database, respectively.

Analysis of methods and technologies of human face recognition

The object of research is the processes of biometric identification and human authentication based on the image of his face for computer vision systems. One of the most problematic places in biometric identification systems using computer vision is the problem of eliminating ambiguity of «scanning». Such ambiguity arises when designing three-dimensional objects of the real world on flat images. In the course of the research, the results of the analysis of the effects of requirements and factors on the features and characteristics of the object of the biometric face recognition system are used. First of all, it is the variability of visual images, the design of three-dimensional objects, the number and location of light sources, the color and intensity of radiation, shadows or reflections from surrounding objects. The solution to the problem of detecting objects on the image lies in the correct choice of the description of objects, for the detection and recognition of which the system is created. Analysis of the features of classes and the properties of face recognition tasks shows that it is sufficient for a database of authentication systems to store a small set of predefined key characteristics, as much as possible characterize the images. Thus, by configuring the system to reduce the probability of incorrect identification, it is possible to use several images belonging to one person. For such purposes, a video sequence of certain specific head movements and facial muscles of the face is sufficient. A generalized algorithm for automatic face detection and recognition is developed. The presented scheme of the generalized algorithm consists of nine simple steps and takes into account the identification features using photo and video images. The advantage of the algorithm is the simplicity of implementation, it allows already at the design stage of the identification system, to quickly evaluate the system's operability by analyzing the internal interaction of its elements.

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

<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>

Effects of surface materials on polarimetric-thermal measurements: applications to face recognition.

Materials, such as cosmetics, applied to the face can severely inhibit biometric face-recognition systems operating in the visible spectrum. These products are typically made up of materials having different spectral properties and color pigmentation that distorts the perceived shape of the face. The surface of the face emits thermal radiation, due to the living tissue beneath the surface of the skin. The emissivity of skin is approximately 0.99; in comparison, oil- and plastic-based materials, commonly found in cosmetics and face paints, have an emissivity range of 0.9-0.95 in the long-wavelength infrared part of the spectrum. Due to these properties, all three are good thermal emitters and have little impact on the heat transferred from the face. Polarimetric-thermal imaging provides additional details of the face and is also dependent upon the thermal radiation from the face. In this paper, we provide a theoretical analysis on the thermal conductivity of various materials commonly applied to the face using a metallic sphere. Additionally, we observe the impact of environmental conditions on the strength of the polarimetric signature and the ability to recover geometric details. Finally, we show how these materials degrade the performance of traditional face-recognition methods and provide an approach to mitigating this effect using polarimetric-thermal imaging.

Face Recognition across Time Lapse Using Convolutional Neural Networks

Time lapse, characteristic of aging, is a complex process that affects the reliability and security of biometric face recognition systems. This paper reports the novel use and effectiveness of deep learning, in general, and convolutional neural networks (CNN), in particular, for automatic rather than hand-crafted feature extraction for robust face recognition across time lapse. A CNN architecture using the VGG-Face deep (neural network) learning is found to produce highly discriminative and interoperable features that are robust to aging variations even across a mix of biometric datasets. The features extracted show high inter-class and low intra-class variability leading to low generalization errors on aging datasets using ensembles of subspace discriminant classifiers. The classification results for the all-encompassing authentication methods proposed on the challenging FG-NET and MORPH datasets are competitive with state-of-the-art methods including commercial face recognition engines and are richer in functionality and interoperability than existing methods as it handles mixed biometric datasets, e.g., FG-NET and MORPH.

A motion and similarity-based fake detection method for biometric face recognition systems

In this paper, a motion and similarity-based fake detection algorithm is presented for biometric face recognition systems. First, an input video is segmented into foreground and background regions. Second, the similarity is measured between a background region, i.e., a region without a face and upper body, and an original background region recorded at an initializing stage. Third, a background motion index is calculated to indicate the amount of motion in the background region compared with the motion in the foreground region. By combining the result of similarity and the result of the background motion index, a fake video can be detected robustly with a regular USB camera.

US concept wars, civil liberties and the technologies of fortification

The horrific events of September 11, 2001 led to calls for the deployment of state-of-the-art security systems and the creation of an “invisible shield” to protect America. Such proposals immediately triggered debate about the constitutional ethics of surveillance in the United States. While there has been widespread support for the surrender of some cherished civil liberties in the war against terror, surveillance, especially the visual variety, is still seen to be innately un-American. Technologies like biometric face recognition systems, critics argue, are not consistent with the values of an open society and should consequently be considered with great caution. The widespread introduction of such invasive technology, pundits claim, would signal a victory for the terrorists. Many American commentators fail to recognize, however, that the United States, far from being the open society they imagine, has long been characterized by exclusion. Moreover, such exclusion is increasingly enforced by the same high technology of which biometrics is a part. In fact, for at least a decade before the declaration of the war on terror, the United States has been undergoing a multifaceted process of fortification both within its cities and at the US-Mexican frontier. Investigation of this phenomena reveals the central role of the US’s longest running concept war, the war on drugs. This paper argues that within this narrative biometric surveillance and the creation of “an invisible wall” should not be regarded as a break with American traditions but as the next organic phase of an ongoing process of fortifying the United States.