Automated border control e-gates and facial recognition systems

Joint and collaborative representation with local adaptive convolution feature for face recognition with single sample per person

Singular value decomposition and local near neighbors for face recognition under varying illumination

Face recognition is receiving a significant attention due to the need of facing important challenges when developing real applications under unconstrained environments. The three most important challenges are facial occlusion, the problem of dealing with a single sample per subject (SSPS) and facial expression. This paper describes and analyzes various strategies that have been developed recently for overcoming these three major challenges that seriously affect the performance of real face recognition systems. This survey is organized in three parts. In the first part, approaches to tackle the challenge of facial occlusion are classified, illustrated and compared. The second part briefly describes the SSPS problem and the associated solutions. In the third part, facial expression challenge is illustrated. In addition, pros and cons of each technique are stated. Finally, several improvements for future research are suggested, providing a useful perspective for addressing new research in face recognition.

A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression

The issue of single sample per person (SSPP) face recognition has attracted more and more attention in recent years. Patch/local-based algorithm is one of the most popular categories to address the issue, as patch/local features are robust to face image variations. However, the global discriminative information is ignored in patch/local-based algorithm, which is crucial to recognize the nondiscriminative region of face images. To make the best of the advantage of both local information and global information, a novel two-layer local-to-global feature learning framework is proposed to address SSPP face recognition. In the first layer, the objective-oriented local features are learned by a patch-based fuzzy rough set feature selection strategy. The obtained local features are not only robust to the image variations, but also usable to preserve the discrimination ability of original patches. Global structural information is extracted from local features by a sparse autoencoder in the second layer, which reduces the negative effect of nondiscriminative regions. Besides, the proposed framework is a shallow network, which avoids the over-fitting caused by using multilayer network to address SSPP problem. The experimental results have shown that the proposed local-to-global feature learning framework can achieve superior performance than other state-of-the-art feature learning algorithms for SSPP face recognition.

/pdf/fuzzy-sparse-autoencoder-framework-for-single-image-per-2htgt4hxzp.pdf

Fuzzy Sparse Autoencoder Framework for Single Image Per Person Face Recognition

A new face recognition method based on image decomposition for single sample per person problem

Vector projection for face recognition

Fisher linear discriminant analysis (FLDA) algorithm is a popular subspace method for face recognition, which requires that the number of training samples for each object is not less than two. In this paper, an adaptive approximation image reconstruction method based on economy singular value decomposition (ESVD) algorithm is proposed for the single sample problem in face recognition. By using ESVD the single training sample is decomposed to a set of basis images. Then an adaptive approximation image reconstruction method is proposed to reconstruct an approximation image by using several significant basis images. The single training image and its approximation image for each object form a new training set, which can make the FLDA be applied to the single sample problem in face recognition. The major contribution of the proposed work is that the number of significant basis images for the reconstruction of an approximation image is evaluated by using a reverse thinking approach based on experimental analysis. The performance of the proposed method is verified on the Yale, FERET, ORL, UMIST and AR face databases. The experimental results indicate that the proposed method is efficient and outperforms some existing methods which are proposed to overcome the single sample problem.

An adaptive approximation image reconstruction method for single sample problem in face recognition using FLDA

The extended sparse representation classifier (ESRC) is one of the state-of-the-art solutions for single sample face recognition, but it performs unsatisfactorily under varying illumination. There are two main reasons: one is that the mutual influences of the reflectance and illumination in intra-class variant bases of the ESRC are extreme under varying illumination, the other is that the specific identity information of the face in the generic set constrains the performance of ESRC, since the specific identity information of the generic face is redundant and should be removed. In this paper, the additive strategy is introduced to ESRC, which can efficiently eliminate aforementioned two issues. Two additive models: one is the reflectance and illumination additive (R&L) model and the other is the high-and low-frequency additive (H&L) model, are introduced to ESRC to obtain two new methods: R&L_ESRC and H&L_ESRC. Each method can be solved by a combined L1-minimization problem, and the final classification is determined by the sum of two weighting residuals: the reflectance and illumination residuals (or the high-and low-frequency residuals). In our experiments, the wavelet denoising (WDM) model and the logarithmic total variation (LTV) model are employed to extract facial features in R&L_ESRC and H&L_ESRC respectively. The performances of the proposed methods are verified on the Extended Yale B, CMU PIE, AR, ORL, and self-built Driver face databases. The experimental results illustrate that the proposed techniques outperform previous approaches under both normal and variant illumination conditions.

Mengjun Ye

Papers

Singular value decomposition and local near neighbors for face recognition under varying illumination

A new face recognition method based on image decomposition for single sample per person problem

Vector projection for face recognition

An adaptive approximation image reconstruction method for single sample problem in face recognition using FLDA

Illumination robust single sample face recognition based on ESRC