Gabriele Sabatino

Fast 3D face recognition based on normal map

This paper presents a 3D face recognition method aimed to biometric applications. The proposed method compares any two faces represented as 3D polygonal surfaces through their corresponding normal map, a bidimensional array which stores local curvature (mesh normals) as the pixels RGB components of a color image. The recognition approach, based on the computation of a difference map resulting from the comparison of normal maps, is simple yet fast and accurate. A weighting mask, automatically generated for each subject using a set of expression variations, improves the robustness to a broad range of facial expressions. First results show the effectiveness of the method on a database of 3D faces featuring different genders, ages and expressions.

3D Face Recognition using Normal Sphere and General Fourier Descriptor

Today, face figures among the most promising biometrics, allowing to identify people without requiring any physical contact. In this research field, 3D provides a significant improvement in recognition performances, but the existing approaches show limitations dealing with pose variations; indeed 3D face surfaces need to be aligned before the matching operation. This paper proposes an approach that overcomes this limitation by projecting the 3D shape information onto the 2D surface of a normal sphere, while a rotation invariant descriptor is used to extract key features from this surface. In addition, using a 2D descriptor reduces the computing time that is a typical drawback of 3D methods. Experimentations have been conducted on a property face dataset, to assess the robustness of the method with respect to a large set of facial expression and pose variations

3D Face Recognition in a Ambient Intelligence Environment Scenario

Information and Communication Technologies are increasingly entering in all aspects of our life and in all sectors, opening a world of unprecedented scenarios where people interact with electronic devices embedded in environments that are sensitive and responsive to the presence of users. Indeed, since the first examples of “intelligent” buildings featuring computer aided security and fire safety systems, the request for more sophisticated services, provided according to each user’s specific needs has characterized the new tendencies within domotic research. The result of the evolution of the original concept of home automation is known as Ambient Intelligence (Aarts & Marzano, 2003), referring to an environment viewed as a “community” of smart objects powered by computational capability and high user-friendliness, capable of recognizing and responding to the presence of different individuals in a seamless, not-intrusive and often invisible way. As adaptivity here is the key for providing customized services, the role of person sensing and recognition become of fundamental importance. This scenario offers the opportunity to exploit the potential of face as a not intrusive biometric identifier to not just regulate access to the controlled environment but to adapt the provided services to the preferences of the recognized user. Biometric recognition (Maltoni et al., 2003) refers to the use of distinctive physiological (e.g., fingerprints, face, retina, iris) and behavioural (e.g., gait, signature) characteristics, called biometric identifiers, for automatically recognizing individuals. Because biometric identifiers cannot be easily misplaced, forged, or shared, they are considered more reliable for person recognition than traditional token or knowledge-based methods. Others typical objectives of biometric recognition are user convenience (e.g., service access without a Personal Identification Number), better security (e.g., difficult to forge access). All these reasons make biometrics very suited for Ambient Intelligence applications, and this is specially true for a biometric identifier such as face which is one of the most common methods of recognition that humans use in their visual interactions, and allows to recognize the user in a not intrusive way without any physical contact with the sensor. A generic biometric system could operate either in verification or identification modality, better known as one-to-one and one-to-many recognition (Perronnin & Dugelay, 2003). In the proposed Ambient Intelligence application we are interested in one-to-one recognition,

GPU accelerated 3D face registration / recognition

This paper proposes a novel approach to both registration and recognition of face in three dimensions. The presented method is based on normal map metric to perform either the alignment of captured face to a reference template or the comparison between any two faces in a gallery. As the metric involved is highly suited to be computed via vector processor, we propose an implementation of the whole framework on last generation graphics boards, to exploit the potential of GPUs applied to large scale biometric identification applications. This work shows how the use of affordable consumer grade hardware could allow ultra rapid comparison between face descriptors through their highly specialized architecture. The approach also addresses facial expression changes by means of a subject specific weighting masks. We include preliminary results of experiments conducted on a proprietary gallery and on a subset of FRGC database.

Fast 3D Face Alignment and Improved Recognition Through Pyramidal Normal map Metric

Faces tri-dimensional shape represents a highly discriminating yet challenging biometric identifier due to different issues, some of which related to capture, alignment and normalization. This paper presents an improved normal map based face recognition approach, which relies on a novel method to automatically align a captured 3D face mesh to a reference template, allowing a more precise face comparison. The alignment algorithm exploits pyramidal-normal-map metric, a coarse to finer measurement of angular distance between two surfaces computed through normal maps with progressively increasing resolution. After the registration has been performed, the normalized face can be rapidly compared to any other template in the gallery database for authentication or identification purposes using standard normal map metric. The alignment approach avoids the need for a rough or manual face pre-alignment and maximizes recognition precision, requiring a fraction of the time needed by the iterative closest point (ICP) method to operate. We show preliminary experimental results on a 3D dataset featuring 235 different subjects.

Ultra fast GPU assisted face recognition based on 3d geometry and texture data

Face recognition represents a challenging research topic which has been investigated by means of many techniques operating either in 2D or 3D, and, more recently, even through multi-modal approaches. Whatever the methodology used to compare any two faces, the main concern has been on recognition accuracy, often disregarding the efficiency issue which may be crucial in a large scale one-to-many recognition application. This paper presents a Graphic Processing Unit (GPU) assisted face recognition method, operating on 4D data (geometry + texture). It exploits augmented normal map, a 32 bit deep color bitmap, as face descriptor, allowing ultra fast face comparison through the specialized hardware (pixel shaders) available in almost any recently designed PC graphic boards. The proposed approach addresses facial expression changes and presence of beard by means of two (subject specific) filtering masks. We include preliminary experimental results on a large gallery of faces.

One to many 3D face recognition enhanced through k-d -tree based spatial access

Most face based biometric systems and the underlying recognition algorithms are often more suited for verification (one-to-one comparison) instead of identification (one-to-many comparison) purposes. This is even more true in case of large face database, as the computational cost of an accurate comparison between the query and a gallery of many thousands of individuals could be too high for practical applications. In this paper we present a 3D based face recognition method which relies on normal image to represent and compare face geometry. It features fast comparison time and good robustness to a wide range of expressive variations thanks to an expression weighting mask, automatically generated for each enrolled subject. To better address one-to-many recognition applications, the proposed approach is improved via DFT based indexing of face descriptors and k-d-tree based spatial access to clusters of similar faces. We include experimental results showing the effectiveness of the presented method in terms of recognition accuracy and the improvements in one-to-many recognition time achieved thanks to indexing and retrieval techniques applied to a large parametric 3D face database.

A system to support the integrated management of diagnostic medical images

Information systems are essential tools supporting the management of hospital organizations. The demand for availability and integration of data in this field is more and more increasing, basically for absolving two key issues: collecting and merging all the elementary data available for a patient during the hospitalization process, so that physicians and other operators get all the necessary information they need during the process; planning the development of the diagnostic activities/therapeutics to optimize the process. In this perspective, we present a system that integrates a booking subsystem for hospital specialized treatments booking (CUP), a subsystem for the management of the hospitalizations (including First Aid Departments), a subsystem for filing and reporting clinical images, a subsystem for the analysis of radiological images in a unique management environment. Therefore we describe a complete system for the management of an archive of digital dossiers for the patients of a hospital, where diagnostic imaging is a central issue.