The pose problem is one of the bottlenecks in automatic face recognition. We argue that one of the diffculties in this problem is the severe misalignment in face images or feature vectors with different poses. In this paper, we propose that this problem can be statistically solved or at least mitigated by maximizing the intra-subject across-pose correlations via canonical correlation analysis (CCA). In our method, based on the data set with coupled face images of the same identities and across two different poses, CCA learns simultaneously two linear transforms, each for one pose. In the transformed subspace, the intra-subject correlations between the different poses are maximized, which implies pose-invariance or pose-robustness is achieved. The experimental results show that our approach could considerably improve the recognition performance. And if further enhanced with holistic+local feature representation, the performance could be comparable to the state-of-the-art.
Besides building the pose-robust statistical models, statistically transforming face or features from one pose to another is another way to tackle the pose problem. @cite_16 transformed the frontal face model to non-frontal views for extending the gallery set. Lee and Kim @cite_12 constructed feature spaces for each pose using kernel principal component analysis, and then transformed the non-frontal face to frontal through the feature spaces. Different from the foregoing two methods that transform holistic faces, @cite_20 performed linear regression on local patches for virtual frontal view synthesis. @cite_5 embedded bias-variance trade-off in the cross-pose linear regression models by using ridge and lasso regression. Such bias-variance trade-off achieved considerable improvements in recognition performance. @cite_11 applied null space linear discriminant analysis in their face recognition approach dealing with both pose and illumination variations.
@cite_5 @cite_16 @cite_12 @cite_20 @cite_11
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We address the pose mismatch problem which can occur in face verification systems that have only a single (frontal) face image available for training. In the framework of a Bayesian classifier based on mixtures of gaussians, the problem is tackled through extending each frontal face model with artificially synthesized models for non-frontal views. The synthesis methods are based on several implementations of maximum likelihood linear regression (MLLR), as well as standard multi-variate linear regression (LinReg). All synthesis techniques rely on prior information and learn how face models for the frontal view are related to face models for non-frontal views. The synthesis and extension approach is evaluated by applying it to two face verification systems: a holistic system (based on PCA-derived features) and a local feature system (based on DCT-derived features). Experiments on the FERET database suggest that for the holistic system, the LinReg-based technique is more suited than the MLLR-based techniques; for the local feature system, the results show that synthesis via a new MLLR implementation obtains better performance than synthesis based on traditional MLLR. The results further suggest that extending frontal models considerably reduces errors. It is also shown that the local feature system is less affected by view changes than the holistic system; this can be attributed to the parts based representation of the face, and, due to the classifier based on mixtures of gaussians, the lack of constraints on spatial relations between the face parts, allowing for deformations and movements of face areas. The variation of facial appearance due to the viewpoint ( pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method. 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. Face recognition under varying pose is a challenging problem, especially when illumination variations are also present. In this paper, we propose to address one of the most challenging scenarios in face recognition. That is, to identify a subject from a test image that is acquired under dierent pose and illumination condition from only one training sample (also known as a gallery image) of this subject in the database. For example, the test image could be semifrontal and illuminated by multiple lighting sources while the corresponding training image is frontal under a single lighting source. Under the assumption of Lambertian reflectance, the spherical harmonics representation has proved to be effective in modeling illumination variations for a fixed pose. In this paper, we extend the spherical harmonics representation to encode pose information. More specifically, we utilize the fact that 2D harmonic basis images at different poses are related by close-form linear transformations, and give a more convenient transformation matrix to be directly used for basis images. An immediate application is that we can easily synthesize a different view of a subject under arbitrary lighting conditions by changing the coefficients of the spherical harmonics representation. A more important result is an efficient face recognition method, based on the orthonormality of the linear transformations, for solving the above-mentioned challenging scenario. Thus, we directly project a nonfrontal view test image onto the space of frontal view harmonic basis images. The impact of some empirical factors due to the projection is embedded in a sparse warping matrix; for most cases, we show that the recognition performance does not deteriorate after warping the test image to the frontal view. Very good recognition results are obtained using this method for both synthetic and challenging real images.