Face Recognition from One Example View

If we are provided a face database with only one example view per person, is it possible to recognize new views of them under a variety of different poses, especially views rotated in depth from the original example view? We investigate using prior knowledge about faces plus each single example view to generate virtual views of each person, or views of the face as seen from different poses. Prior knowledge of faces is represented in an example-based way, using 2D views of a prototype face seen rotating in depth. The synthesized virtual views are evaluated as example views in a view-based approach to pose-invariant face recognition. They are shown to improve the recognition rate over the scenario where only the single real view is used

3D Object Recognition and Facial Identification Using Time-averaged Single-views from Time-of-flight 3D Depth-Camera

International audienceWe report here on feasibility evaluation experiments for 3D object recognition and person facial identification from single-view on real depth images acquired with an “off-the-shelf” 3D time-of-flight depth camera. Our methodology is the following: for each person or object, we perform 2 independent recordings, one used for learning and the other one for test purposes. For each recorded frame, a 3D-mesh is computed by simple triangulation from the filtered depth image. The feature we use for recognition is the normalized histogram of directions of normal vectors to the 3D-mesh facets. We consider each training frame as a separate example, and the training is done with a multilayer perceptron with 1 hidden layer. For our 3D person facial identification experiments, 3 different persons were used, and we obtain a global correct rank-1 recognition rate of up to 80%, measured on test frames from an independent 3D video. For our 3D object recognition experiment, we have considered 3 different objects, and obtain a correct single-frame recognition rate of 95%, and checked that the method is quite robust to variation of distance from depth camera to object. These first experiments show that 3D object recognition or 3D face identification, with a time-of-flight 3D camera, seems feasible, despite the high level of noise in the obtained real depth images

Pose-Invariant Face Recognition Using Real and Virtual Views

The problem of automatic face recognition is to visually identify a person in an input image. This task is performed by matching the input face against the faces of known people in a database of faces. Most existing work in face recognition has limited the scope of the problem, however, by dealing primarily with frontal views, neutral expressions, and fixed lighting conditions. To help generalize existing face recognition systems, we look at the problem of recognizing faces under a range of viewpoints. In particular, we consider two cases of this problem: (i) many example views are available of each person, and (ii) only one view is available per person, perhaps a driver's license or passport photograph. Ideally, we would like to address these two cases using a simple view-based approach, where a person is represented in the database by using a number of views on the viewing sphere. While the view-based approach is consistent with case (i), for case (ii) we need to augment the single real view of each person with synthetic views from other viewpoints, views we call 'virtual views'. Virtual views are generated using prior knowledge of face rotation, knowledge that is 'learned' from images of prototype faces. This prior knowledge is used to effectively rotate in depth the single real view available of each person. In this thesis, I present the view-based face recognizer, techniques for synthesizing virtual views, and experimental results using real and virtual views in the recognizer

Vision-based techniques for gait recognition

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance

System and technique for retrieving depth information about a surface by projecting a composite image of modulated light patterns

A technique, associated system and program code, for retrieving depth information about at least one surface of an object. Core features include: projecting a composite image comprising a plurality of modulated structured light patterns, at the object; capturing an image reflected from the surface; and recovering pattern information from the reflected image, for each of the modulated structured light patterns. Pattern information is preferably recovered for each modulated structured light pattern used to create the composite, by performing a demodulation of the reflected image. Reconstruction of the surface can be accomplished by using depth information from the recovered patterns to produce a depth map/mapping thereof. Each signal waveform used for the modulation of a respective structured light pattern, is distinct from each of the other signal waveforms used for the modulation of other structured light patterns of a composite image; these signal waveforms may be selected from suitable types in any combination of distinct signal waveforms, provided the waveforms used are uncorrelated with respect to each other. The depth map/mapping to be utilized in a host of applications, for example: displaying a 3-D view of the object; virtual reality user-interaction interface with a computerized device; face--or other animal feature or inanimate object--recognition and comparison techniques for security or identification purposes; and 3-D video teleconferencing/telecollaboration

System and technique for retrieving depth information about a surface by projecting a composite image of modulated light patterns

A technique, associated system and program code, for retrieving depth information about at least one surface of an object, such as an anatomical feature. Core features include: projecting a composite image comprising a plurality of modulated structured light patterns, at the anatomical feature; capturing an image reflected from the surface; and recovering pattern information from the reflected image, for each of the modulated structured light patterns. Pattern information is preferably recovered for each modulated structured light pattern used to create the composite, by performing a demodulation of the reflected image. Reconstruction of the surface can be accomplished by using depth information from the recovered patterns to produce a depth map/mapping thereof. Each signal waveform used for the modulation of a respective structured light pattern, is distinct from each of the other signal waveforms used for the modulation of other structured light patterns of a composite image; these signal waveforms may be selected from suitable types in any combination of distinct signal waveforms, provided the waveforms used are uncorrelated with respect to each other. The depth map/mapping to be utilized in a host of applications, for example: displaying a 3-D view of the object; virtual reality user-interaction interface with a computerized device; face--or other animal feature or inanimate object--recognition and comparison techniques for security or identification purposes; and 3-D video teleconferencing/telecollaboration

The effect of image pixelation on unfamiliar face matching

Low-resolution, pixelated images from CCTV can be used to compare the perpetrators of crime with high-resolution photographs of potential suspects. The current study investigated the accuracy of person identification under these conditions, by comparing high-resolution and pixelated photographs of unfamiliar faces in a series of matching tasks. Performance decreased gradually with different levels of pixelation and was close to chance with a horizontal image resolution of only 8 pixel bands per face (Experiment 1). Matching accuracy could be improved by reducing the size of pixelated faces (Experiment 2) or by varying the size of the to-be-compared-with high-resolution face image (Experiment 3). In addition, pixelation produced effects that appear to be separable from other factors that might affect matching performance, such as changes in face view (Experiment 4). These findings reaffirm that criminal identifications from CCTV must be treated with caution and provide some basic estimates for identification accuracy with different pixelation levels. This study also highlights potential methods for improving performance in this task

Recognising the ageing face: the role of age in face processing

The effects of age-induced changes on face recognition were investigated as a means of exploring the role of age in the encoding of new facial memories. The ability of participants to recognise each of six previously learnt faces was tested with versions which were either identical to the learnt faces, the same age (but different in pose and expression), or younger or older in age. Participants were able to cope well with facial changes induced by ageing: their performance with older, but not younger, versions was comparable to that with faces which differed only in pose and expression. Since the large majority of different age versions were recognised successfully, it can be concluded that the process of recognition does not require an exact match in age characteristics between the stored representation of a face and the face currently in view. As the age-related changes explored here were those that occur during the period of growth, this in turn implies that the underlying structural physical properties of the face are (in addition to pose and facial expression) invariant to a certain extent