Lansun Shen

Bio: Lansun Shen is an academic researcher from Beijing University of Technology. The author has contributed to research in topics: Facial recognition system & Video quality. The author has an hindex of 11, co-authored 42 publications receiving 523 citations.

Optimal sampling of Gabor features for face recognition

Abstract: The Gabor feature is effective for facial image representation, while linear discriminant analysis (LDA) can extract the most discriminant information from the Gabor feature for face recognition In practice, the dimension of a Gabor feature vector is so high that the computation and memory requirements are prohibitively large To reduce the dimension, one simple scheme is to extract the Gabor feature at sub-sampled positions, usually in a regular grid, in a face region However, this scheme is not effective enough and degrades the recognition performance In this paper, we propose a method to determine the optimal position for extracting the Gabor feature such that the number of feature points is as small as possible while the representation capability of the points is as high as possible The subsampled positions of the feature points are determined by a mask generated from a set of training images by means of principal component analysis (PCA) With the feature vector of reduced dimension, a subspace LDA is applied for face recognition, ie, PCA is first used to reduce the dimension of the Gabor feature vectors generated from the subsampled positions, and then a common LDA is applied Experimental results show that the new sampling method is simple, and effective for both dimension reduction and image representation The recognition rate based on our proposed scheme is also higher than that achieved using a regular sampling method in a face region

Illumination invariant face recognition

Abstract: The appearance of a face will vary drastically when the illumination changes. Variations in lighting conditions make face recognition an even more challenging and difficult task. In this paper, we propose a novel approach to handle the illumination problem. Our method can restore a face image captured under arbitrary lighting conditions to one with frontal illumination by using a ratio-image between the face image and a reference face image, both of which are blurred by a Gaussian filter. An iterative algorithm is then used to update the reference image, which is reconstructed from the restored image by means of principal component analysis (PCA), in order to obtain a visually better restored image. Image processing techniques are also used to improve the quality of the restored image. To evaluate the performance of our algorithm, restored images with frontal illumination are used for face recognition by means of PCA. Experimental results demonstrate that face recognition using our method can achieve a higher recognition rate based on the Yale B database and the Yale database. Our algorithm has several advantages over other previous algorithms: (1) it does not need to estimate the face surface normals and the light source directions, (2) it does not need many images captured under different lighting conditions for each person, nor a set of bootstrap images that includes many images with different illuminations, and (3) it does not need to detect accurate positions of some facial feature points or to warp the image for alignment, etc.

Example-based image super-resolution with class-specific predictors

Abstract: Example-based super-resolution is a promising approach to solving the image super-resolution problem. However, the learning process can be slow and prediction can be inaccurate. In this paper, we present a novel learning-based algorithm for image super-resolution to improve the computational speed and prediction accuracy. Our new method classifies image patches into several classes, for each class, a class-specific predictor is designed. A class-specific predictor takes a low-resolution image patch as input and predicts a corresponding high-resolution patch as output. The performances of the class-specific predictors are evaluated using different datasets formed by face images and natural-scene images. We present experimental results which demonstrate that the new method provides improved performances over existing methods.

An adaptive algorithm for the display of high-dynamic range images

Abstract: A novel algorithm based on spatial and statistical information is proposed for the display of high-dynamic range (HDR) images. In our proposed algorithm, an image is first decomposed into a base layer and a detailed layer, which represent its smoothed and fine details, respectively. The problem of overall impression preservation is regarded as a global issue in our algorithm. Statistical-based histogram adjustment is employed to deal with the base layer. The reproduction of visual details is regarded as a local issue. The detailed layer obtained using a spatial filter is adaptively enhanced according to the mapping function used for the base layer. The main contributions of our algorithm are that: (1) an adaptive detail-enhancement method is proposed and (2) a gain map is defined to combine the local and global issues. Experimental results show the superior performance of our approach in terms of visual quality.

An efficient example-based approach for image super-resolution

Abstract: A novel algorithm for image super-resolution with class-specific predictors is proposed in this paper. In our algorithm, the training example images are classified into several classes, and each patch of a low-resolution image is classified into one of these classes. Each class has its high-frequency information inferred using a class-specific predictor, which is trained via the training samples from the same class. In this paper, two different types of training sets are employed to investigate the impact of the training database to be used. Experimental results have shown the superior performance of our method.

Introduction to Recommender Systems Handbook

Abstract: Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.

Super-resolution: a comprehensive survey

Abstract: Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real-world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

Organic Photodiodes: The Future of Full Color Detection and Image Sensing

Abstract: Major growth in the image sensor market is largely as a result of the expansion of digital imaging into cameras, whether stand-alone or integrated within smart cellular phones or automotive vehicles. Applications in biomedicine, education, environmental monitoring, optical communications, pharmaceutics and machine vision are also driving the development of imaging technologies. Organic photodiodes (OPDs) are now being investigated for existing imaging technologies, as their properties make them interesting candidates for these applications. OPDs offer cheaper processing methods, devices that are light, flexible and compatible with large (or small) areas, and the ability to tune the photophysical and optoelectronic properties − both at a material and device level. Although the concept of OPDs has been around for some time, it is only relatively recently that significant progress has been made, with their performance now reaching the point that they are beginning to rival their inorganic counterparts in a number of performance criteria including the linear dynamic range, detectivity, and color selectivity. This review covers the progress made in the OPD field, describing their development as well as the challenges and opportunities.

A review on Gabor wavelets for face recognition

Abstract: Due to the robustness of Gabor features against local distortions caused by variance of illumination, expression and pose, they have been successfully applied for face recognition. The Facial Recognition Technology (FERET) evaluation and the recent Face Verification Competition (FVC2004) have seen the top performance of Gabor feature based methods. This paper aims to give a detailed survey of state of the art 2D face recognition algorithms using Gabor wavelets for feature extraction. Existing problems are covered and possible solutions are suggested.

Hierarchical Ensemble of Global and Local Classifiers for Face Recognition

Abstract: In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.