Enrico Grosso

On the Use of SIFT Features for Face Authentication

Several pattern recognition and classification techniques have been applied to the biometrics domain. Among them, an interesting technique is the Scale Invariant Feature Transform (SIFT), originally devised for object recognition. Even if SIFT features have emerged as a very powerful image descriptors, their employment in face analysis context has never been systematically investigated. This paper investigates the application of the SIFT approach in the context of face authentication. In order to determine the real potential and applicability of the method, different matching schemes are proposed and tested using the BANCA database and protocol, showing promising results.

Face Identification by SIFT-based Complete Graph Topology

This paper presents a new face identification system based on graph matching technique on SIFT features extracted from face images. Although SIFT features have been successfully used for general object detection and recognition, only recently they were applied to face recognition. This paper further investigates the performance of identification techniques based on Graph matching topology drawn on SIFT features which are invariant to rotation, scaling and translation. Face projections on images, represented by a graph, can be matched onto new images by maximizing a similarity function taking into account spatial distortions and the similarities of the local features. Two graph based matching techniques have been investigated to deal with false pair assignment and reducing the number of features to find the optimal feature set between database and query face SIFT features. The experimental results, performed on the BANCA database, demonstrate the effectiveness of the proposed system for automatic face identification.

Robust visual servoing in 3-D reaching tasks

This paper describes a novel approach to the problem of reaching an object in space under visual guidance. The approach is characterized by a great robustness to calibration errors, such that virtually no calibration is required. Servoing is based on binocular vision: a continuous measure of the end-effector motion field, derived from real-time computation of the binocular optical flow over the stereo images, is compared with the actual position of the target and the relative error in the end-effector trajectory is continuously corrected. The paper outlines the general framework of the approach, shows how visual measures are obtained and discusses the synthesis of the controller along with its stability analysis. Real-time experiments are presented to show the applicability of the approach in real 3-D applications.

3D object reconstruction using stereo and motion

The extraction of reliable range data from images is investigated, considering, as a possible solution, the integration of different sensor modalities. Two different algorithms are used to obtain independent estimates of depth from a sequence of stereo images. The results are integrated on the basis of the uncertainty of each measure. The stereo algorithm uses a coarse-to-fine control strategy to compute disparity. An algorithm for depth-from-motion is used, exploiting the constraint imposed by active motion of the cameras. To obtain a 3D description of the objects, the motion of the cameras is purposefully controlled, in such a manner as to move around the objects in view while the gaze is directed toward a fixed point in space. This egomotion strategy, which is similar to that adopted by the human visuomotor system, allows a better exploration of partially occluded objects and simplifies the motion equations. When tested on real scenes, the algorithm demonstrated a low sensitivity to image noise, mainly due to the integration of independent measures. An experiment performed on a real scene containing several objects is presented. >

Active/dynamic stereo vision

Visual navigation is a challenging issue in automated robot control. In many robot applications, like object manipulation in hazardous environments or autonomous locomotion, it is necessary to automatically detect and avoid obstacles while planning a safe trajectory. In this context the detection of corridors of free space along the robot trajectory is a very important capability which requires nontrivial visual processing. In most cases it is possible to take advantage of the active control of the cameras. In this paper we propose a cooperative schema in which motion and stereo vision are used to infer scene structure and determine free space areas. Binocular disparity, computed on several stereo images over time, is combined with optical flow from the same sequence to obtain a relative-depth map of the scene. Both the time to impact and depth scaled by the distance of the camera from the fixation point in space are considered as good, relative measurements which are based on the viewer, but centered on the environment. The need for calibrated parameters is considerably reduced by using an active control strategy. The cameras track a point in space independently of the robot motion and the full rotation of the head, which includes the unknown robot motion, is derived from binocular image data. The feasibility of the approach in real robotic applications is demonstrated by several experiments performed on real image data acquired from an autonomous vehicle and a prototype camera head. >

Active vision-based face authentication

Abstract The use of biometric data for automated identity verification, is one of the major challenges in secure access control systems. In this paper, several issues related to the application of active vision techniques for identity verification, using facial images, are discussed and a practical system (developed within an European research project), encompassing the active vision paradigm, is described. The system, originally devised for banking applications, uses a pair of active tracking cameras to fixate the face of the subject and extract space-variant images (namely “fixations”) from the most relevant facial features. These features are automatically extracted with a two-level algorithm which uses a morphological filtering stage for a coarse localization, followed by an adaptive template matching. A simple matching algorithm, based on a space-variant representation of facial features, is applied for identity verification and compared with a technique based on the Principal Component Analysis. Several experiments on identity verification, performed on real images, are presented.

A stereo vision system for real time obstacle avoidance in unknown environment

A stereo vision system for detecting and avoiding obstacles in real time in an unknown environment is described. This is only the low level of the control system architecture of a mobile robot based on the Brooks subsumption architecture. This level is based on a stereo vision algorithm that uses precomputed measurement of the ground floor disparity, and online acquired grey-level stereo images. The floor is supposed to be planar. The algorithm has been successfully implemented on a mobile vehicle, that is capable of moving around in an unpredictable environment, like a laboratory, without collision.<<ETX>>

Dynamic face recognition: From human to machine vision

As confirmed by recent neurophysiological studies, the use of dynamic information is extremely important for humans in visual perception of biological forms and motion. Apart from the mere computation of the visual motion of the viewed objects, the motion itself conveys far more information, which helps understanding the scene. This paper provides an overview and some new insights on the use of dynamic visual information for face recognition. In this context, not only physical features emerge in the face representation, but also behavioral features should be accounted. While physical features are obtained from the subjects face appearance, behavioral features are obtained from the individual motion and articulation of the face. In order to capture both the face appearance and the face dynamics, a dynamical face model based on a combination of Hidden Markov Models is presented. The number of states (or facial expressions) are automatically determined from the data by unsupervised clustering of expressions of faces in the video. The underlying architecture closely recalls the neural patterns activated in the perception of moving faces. Experimental results obtained from real video image data show the feasibility of the proposed approach.

Dynamic stereo in visual navigation

A cooperative schema is proposed in which binocular disparity, computed on several stereo images over time, is combined with optical flow from the same sequence to obtain a relative-depth map of the scene. Both time-to-impact and depth scaled by the distance of the camera from the fixation point in space are considered as good, relative measurements which are based on the viewer (but centered on the environment). Two experiments, performed on image sequences from real scenes, are presented.<<ETX>>

Advanced studies in biometrics : Summer school on biometrics, Alghero, Italy, June 2-6, 2003 : Revised selected lectures and papers

Combining Biometric Evidence for Person Authentication.- Combining Biometric Evidence for Person Authentication.- Biometric Gait Recognition.- A Tutorial on Fingerprint Recognition.- Spiral Topologies for Biometric Recognition.- Statistical Learning Approaches with Application to Face Detection.- Hand Detection by Direct Convexity Estimation.- Template-Based Hand Detection and Tracking.- Student Papers.- 3D Face Recognition Using Stereoscopic Vision.- Selection of Location, Frequency, and Orientation Parameters of 2D Gabor Wavelets for Face Recognition.- A Face Recognition System Based on Local Feature Characterization.- Influence of Location over Several Classifiers in 2D and 3D Face Verification.