Subspace-based Face Recognition Research Articles

Cost-Sensitive Subspace Analysis and Extensions for Face Recognition

Conventional subspace-based face recognition methods seek low-dimensional feature subspaces to achieve high classification accuracy and assume the same loss from different types of misclassification. This assumption, however, may not hold in many practical face recognition systems as different types of misclassification could lead to different losses. Motivated by this concern, this paper proposes a cost-sensitive subspace analysis approach for face recognition. Our approach uses a cost matrix specifying different costs corresponding to different types of misclassifications, into two popular and widely used discriminative subspace analysis methods and devises the cost-sensitive linear discriminant analysis (CSLDA) and cost-sensitive marginal fisher analysis (CSMFA) methods, to achieve a minimum overall recognition loss by performing recognition in these learned low-dimensional subspaces. To better exploit the complementary information from multiple features for improved face recognition, we further propose a multiview cost-sensitive subspace analysis approach by seeking a common feature subspace to fuse multiple face features to improve the recognition performance. Extensive experimental results demonstrate the effectiveness of our proposed methods.

An illumination tolerant class specific 2D subspace based face recognition technique using optimum correlation filter

This paper presents a technique of frequency domain correlation for illumination tolerant face recognition. A pair of correlation filters are generated from input image projected onto class specific subspace, called optimum projecting image correlation filter and from reconstructed image, called reconstructed correlation filter. As the test face image dynamically configures the nature of these two filters, illumination tolerance is achieved. A sharp peak at the correlation plane measured by peak to sidelobe ratio indicates the detection of test face image. The performance of the proposed strategy is evaluated on YaleB and PIE database. The proposed technique outperforms the other existing correlation filters. A hybrid optical system is used for the hardware implementation of the proposed system.

Subspace Based Face Recognition on Hypercomplex Field

Color information is very important cues for recognition task. In this paper, a novel color face recognition approach based on subspace is proposed. The approach employs hypercomplex to encode color information of different channels simultaneously. Hypercomplex matrix decomposition is then used to construct feature extraction approach. To improve learning efficiency of the algorithm, 3D active deformable model is exploited to generate virtual color face images. Experimental results on CMU PIE database verify the effectiveness of the proposed approach.

Incremental template updating for face recognition in home environments

The extreme variability of faces in smart environment applications, due to continuous changes in terms of pose, illumination and subject appearance (hairstyle, make-up, etc.), requires the relevant mode of variations of the subject's faces to be encoded in the templates and to be continuously updated based on new inputs. This work proposes a new video-based template updating approach suitable for home environments where the image acquisition process is totally unconstrained but a large amount of face data is available for continuous learning. A small set of labeled images is initially used to create the templates and the updating is then totally unsupervised. Although the method is here presented in conjunction with a subspace-based face recognition approach, it can be easily adapted to deal with different kinds of face representations. A thorough performance evaluation is carried out to show the efficacy and reliability of the proposed technique.

Misalignment-Robust Face Recognition

Subspace learning techniques for face recognition have been widely studied in the past three decades. In this paper, we study the problem of general subspace-based face recognition under the scenarios with spatial misalignments and/or image occlusions. For a given subspace derived from training data in a supervised, unsupervised, or semi-supervised manner, the embedding of a new datum and its underlying spatial misalignment parameters are simultaneously inferred by solving a constrained l1 norm optimization problem, which minimizes the l1 error between the misalignment-amended image and the image reconstructed from the given subspace along with its principal complementary subspace. A byproduct of this formulation is the capability to detect the underlying image occlusions. Extensive experiments on spatial misalignment estimation, image occlusion detection, and face recognition with spatial misalignments and/or image occlusions all validate the effectiveness of our proposed general formulation for misalignment-robust face recognition.

Face recognition method based on Gabor wavelet and common vector

The performance of subspace-based face recognition methods is easily affected by variances of lighting,pose and expression.To overcome the limitation,a novel face recognition method was proposed which combined Gabor filter and Common Vector(CV)approach.Gabor filter could better extract the local features of images because of its selectivity on scale and orientation,and it is robust to variances of lighting,pose and expression.The common vector is a linear subspace classification method,which can obtain good classification results by extracting the common property of each class training samples.The experimental results show that the proposed method can obtain good recognition results on ORL and Yale databases.

SUBSPACE LEARNING BASED ON LAPLACIAN EIGENMAPS AND LDA FOR FACE RECOGNITION

Subspace-based face recognition method aims to find a low-dimensional subspace of face appearance embedded in a high-dimensional image space. The differences between different methods lie in their different motivations and objective functions. The objective function of the proposed method is formed by combining the ideas of linear Laplacian eigenmaps and linear discriminant analysis. The actual computation of the subspace reduces to a maximum eigenvalue problem. Major advantage of the proposed method over traditional methods is that it utilizes both local manifold structure information and discriminant information of the training data. Experimental results on the AR face databases demonstrate the effectiveness of the proposed method.