Abstract—This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D space, using computer graphics, and it estimates 3D shape and texture of faces from single images. The estimate is achieved by fitting a statistical, morphable model of 3D faces to images. The model is learned from a set of textured 3D scans of heads. We describe the construction of the morphable model, an algorithm to fit the model to images, and a framework for face identification. In this framework, faces are represented by model parameters for 3D shape and texture. We present results obtained with 4,488 images from the publicly available CMU-PIE database and 1,940 images from the FERET database. Index Terms—Face recognition, shape estimation, deformable model, 3D faces, pose invariance, illumination invariance. æ 1

To create a pose-invariant face recognizer, one strategy is the view-based approach, which uses a set of example views at different poses. But what if we only have one example view available, such as a scanned passport photo -- can we still recognize faces under different poses? Given one example view at a known pose, it is still possible to use the view-based approach by exploiting prior knowledge of faces to generate virtual views, or views of the face as seen from different poses. To represent prior knowledge, we use 2D example views of prototype faces under different rotations. We will develop example-based techniques for applying the rotation seen in the prototypes to essentially "rotate" the single real view which is available. Next, the combined set of one real and multiple virtual views is used as example views in a view-based, pose-invariant face recognizer. Our experiments suggest that for expressing prior knowledge of faces, 2D example-based approaches should be considered ...

The system presented here is a specialized version of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local templates. Different poses are represented by different graphs. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a set of precomputed graphs: the "general face knowledge". The final phase of the matching process can be used to generate composite images of faces and to determine certain features represented in the general face knowledge, such as gender or the presence of glasses or a beard. The graphs can be compared by a similarity function which makes the system efficient in recognizing faces. 1 Introduction Face recognition systems can be subdivided into two main categories [1] depending on the nature of the coding of an input picture and its processing. Schemes that use pixels (grey-level values) as the basis for their coding and various forms of sta...

We introduce a learning algorithm for the weights in a very common class of discrimination functions usually called "weighted average". The learning algorithm can reduce the number of free variables by simple but effective a priori criteria about significant features. Here we apply our algorithm to three tasks of different dimensionality all concerned with face recognition. 1 Introduction Many pattern recognition systems can be roughly divided into two parts, feature extraction and pattern discrimination. In feature extraction an input I is transformed into a vector I k 2 IR N . (In speech recognition I k can, for example, represent the Fourier transformation in a certain time interval in a specific frequency band [14]; in image processing I k could be the filter response of a wavelet-like filter at a certain position in the grey-level picture [11, 15]). In discrimination the input I has to be assigned to a specific class c. The extracted features are used to evaluate certain simila...

. Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. In this paper, we report on the FERET database and the September 1996 FERET test. This test is the third in a series of supervised face-recognition test administered under the FERET program.

The development of face recognition over the past years allows an organization into three types of recognition algorithms, namely frontal, profile, and view-tolerant recognition, depending on the kind of imagery and the according recognition algorithms. While frontal recognition certainly is the classical approach, view-tolerant algorithms usually perform recognition in a more sophisticated fashion by taking into consideration some of the underlying physics, geometry, and statistics. Profile schemes as stand-alone systems have a rather marginal significance for identification. However, they are very practical either for fast coarse presearches of large face databases to reduce the computational load for a subsequent sophisticated algorithm, or as part of a hybrid recognition scheme. Such hybrid approaches have a special status among face recognition systems as they combine different recognition approaches in an either serial or parallel order to overcome the shortcomings of the indivi...

As part of the Face Recognition Technology (FERET) program, the U.S. Army Research Laboratory (ARL) conducted supervised government tests and evaluations of automatic face recognition algorithms. The goal of the tests was to provide an independent method of evaluating algorithms and assessing the state of the art in automatic face recognition. This report describes the design and presents the results of the August1994andMarch1995FERET tests. Results for FERET tests administered by ARL between August 1994 and August 1996 are reported.

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The system presented is part of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local features. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a composition of stored graphs: the face bunch graph. The result of this matching process can be used to generate composite images of faces and to determine facial attributes represented in the bunch graph, such as sex or the presence of glasses or a beard. Keywords: face analysis, sex discrimination, facial attributes, phantom faces, Gabor wavelets, elastic graph matching, bunch graph. 1 Introduction The system presented here has not primarily been designed for face processing or even sex identification. It is rather part of a larger effort to develop a general recognition system, which can be applied to faces [1] as well as to any other class of objects [2]. It has also been shown that it can deal with many differe...

Abstract—Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized “identity ” space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model “tied ” factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric that allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance by using the FERET, XM2VTS, and PIE databases. Recognition performance compares favorably with contemporary approaches. Index Terms—Computing methodologies, pattern recognition, applications, face and gesture recognition. Ç 1