Charles Beumier

Automatic 3D face authentication

Abstract This paper presents automatic face authentication from facial surface analysis. This geometrical approach was motivated by difficulties encountered when considering frontal face recognition. Apart from being less sensitive to viewpoint and lighting conditions, the method exploits information which is complementary to gray level based approaches, enabling the fusion with those techniques. A 3D acquisition system based on structured light and adapted to facial surface capture is presented. It is cheap and fast while offering a sufficient resolution for face recognition purposes. The acquisition system and the 3D face comparison algorithm were designed to be integrated in security applications with cooperative scenario.

BIOMET: a multimodal person authentication database including face, voice, fingerprint, hand and signature modalities

Information technology innovations involve a constant evolution of man-machine interaction modes. Automated authentication of people could be used to better adapt the machine to the user. Security can also be enhanced through a better people authentication. Biometrics appears as a promising tool in these two situations. Different modalities can be envisaged, such as: fingerprint, human face images, hand shape, voice, handwritten signature... In order to take advantage of the particularities of each modality, and to improve the performance of a person authentication system, multimodality can be applied. This motivated the recording of BIOMET, a biometric database with five different modalities, including face, voice, fingerprint, hand and signature data. In this paper, the BIOMET multimodal database for person authentication is described. Details about the acquisition protocols of each modality are given. Preliminary monomodal verification results, obtained on a subcorpus of the BIOMET fingerprint data, are also presented.

Face verification from 3D and grey level clues

Abstract We address in this paper automatic face verification from 3D facial surface and grey level analysis. 3D acquisition is performed by a structured light system, adapted to face capture and allowing grey level acquisition in alignment. The 3D facial shapes are compared and the residual error after 3D matching is used as a first similarity measure. A second similarity measure is derived from grey level comparison. As expected, fusing 3D and intensity information increases verification performances. The acquisition system, the 3D and grey level comparison algorithms were designed to be integrated in security applications in which individuals cooperate.

Automatic Face Authentication from 3D surface

This paper presents automatic face authentication based on facial surface analysis. The success of a previous profile-based approach, exclusively relying on geometrical features of the external contour, led us to consider the full facial surface. This motivation was further supported by the independence of viewpoint and lighting conditions of 3D information. The geometry also carries information which is complementary to grey-level based approaches, supporting the combination with those techniques. The facial surface is captured by a system based on structured light and adapted to face to deliver a cheap, fast and sufficiently precise solution. Typical applications concern security in cooperative situations.

3D face recognition

This paper describes the developments of the RMA/SIC department in 3D face recognition and situates them in the research activities in this field. 3D face recognition appears as a promising approach for biometric person identification, bringing robust and specific features, with easy face detection from depth and quite difficult faking possibilities. The successful development of a prototype for real-time automatic profile identification by the SIC led us to the more complete geometrical analysis of facial surfaces. Several versions of cost effective and fast 3D acquisition systems have been realized to capture facial surfaces. Two databases were created and used to make recognition experiments by matching facial surfaces through planar profile comparison. Our developments and other works confirm that a face recognition system should integrate 3D capabilities to exploit the complementarity of robust geometrical features and normalized grey-level cues.

Automatic face identification

This paper presents the discussion and results in the field of automatic face identification. The implementation possibilities are presented, and the retained choices are motivated. The objective is to identify the person whose image is available from a grey-level camera. The approach is to extract characteristics that will be classified according to extracted characteristics of a database. One section is devoted to the importance of a proper acquisition method, based on profile images. Several sections are more technical and deal with the profile extraction, the computation of the curvature and the way characteristics are derived. This is naturally followed by practical results. Finally, some prespectives are listed to let the present work be integrated in a practical application where several hundred people must be identified.

Automatic Profile Identification

This paper presents a practical implementation of a person verification system based on face profiles in a cooperative environment. The image acquisition constraints are given and motivated. A set of global and local features are automatically extracted from the profile outline of the head. The candidate profile is then compared with profiles of the database to issue a distance from which to accept or reject the claimed individual. To increase robustness, the temporal consistency of the decisions over several shots is examined, what is possible thanks to the overall speed of the developed system.

Digital terrain models derived from digital surface model uniform regions in urban areas

Digital terrain models (DTMs) are of significant interest for applications such as environment planning, flood risk assessment or building detection. A digital surface model (DSM) can be obtained efficiently in both time and cost from light detection and ranging (lidar) acquisition or from digital photogrammetry with aerial or satellite stereoscopic imagery. A DTM can be derived from a DSM if the distinction between ground and non-ground pixels can be automated. We propose in this article a new automatic DSM-to-DTM transform targeting urban areas. Our approach segments the DSM twice: first to get large uniform regions normally corresponding to the road network and attached town squares, and second to obtain smoother areas. Smoother DSM areas overlapping the large regions are selected to populate the DTM, which is then completed by a hierarchical interpolation procedure. As a refinement step, unused smoother regions lying under this DTM are added to create, after interpolation, the final DTM. This approach is positioned relative to the literature about segmentation-based lidar ground filtering. The procedure was developed for DSM rasters. Since the DTM extraction is intended to be applied to large images, special attention was devoted to optimize image processing tasks relative to memory usage and execution time. The proposed development was integrated in a building detection procedure and validated qualitatively in the context of a benchmark on urban object detection of the International Society for Photogrammetry and Remote Sensing (ISPRS). It was also applied to Brussels data for which lidar DTMs are available. The DTM comparison supports the correctness of our solution although difficulties may be encountered in off-terrain regions surrounded by higher regions and some interiors of city blocks. A final test on a rural and peri-urban scene opens positive perspectives for scenes more general than urban areas.

3D Face Recognition

This paper describes the developments of the RMA/SIC department in 3D face recognition and situates them in the research activities in this field. 3D face recognition appears as a promising approach for biometric person identification, bringing robust and specific features, with easy face detection from depth and quite difficult faking possibilities. The successful development of a prototype for real-time automatic profile identification by the SIC led us to the more complete geometrical analysis of facial surfaces. Several versions of cost effective and fast 3D acquisition systems have been realized to capture facial surfaces. Two databases were created and used to make recognition experiments by matching facial surfaces through planar profile comparison. Our developments and other works confirm that a face recognition system should integrate 3D capabilities to exploit the complementarity of robust geometrical features and normalized grey-level cues.

SIC DB: multi-modal database for person authentication

This paper presents a multi-modal database intended for person authentication from multiple cues. It currently contains three sessions of the same 120 individuals, providing profile and frontal color images, 3D facial representations and many French and some English speech utterances. People were selected for their availability so that new sessions will be easily acquired. Individual recognition performances for speech, profile and 3D facial surface modalities are presented. The combination of these experts is the subject of current research.