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How much does a single image reveal about the environment it was taken in? In this paper, we investigate how much of that information can be retrieved from a foreground object, combined with the background (i.e. the visible part of the environment). Assuming it is not perfectly diffuse, the foreground object acts as a complexly shaped andfar-from-perfect mirror An additional challenge is that its...
The acquisition of partial BRDF measurements using light field cameras and several illumination directions raises critical questions regarding the accuracy of inferences based on that data. Therefore, we attempt to verify the quality of the reconstruction of a full BRDF using partial input data. A dataset that provides a densely sampled BRDF was used, both in viewing and illumination directions. We...
While Photometric Stereo (PS) has long been confined to the lab, there has been a recent interest in applying this technique to reconstruct outdoor objects and scenes. Un-fortunately, the most successful outdoor PS techniques typically require gathering either months of data, or waiting for a particular time of the year. In this paper, we analyze the illumination requirements for single-day outdoor...
Partially occluded or illuminated faces pose a significant obstacle for robust, real-world face recognition. The problem of how to characterize the error caused by occlusion or illumination is still a challenging task. There must exist some close relationship between the error metric and error distribution. However, some metric (e.g. Z2-norm) can't characterize this error distribution completely....
In this paper, we address the robust face recognition problem. Recently, trace lasso was introduced as an adaptive norm based on the training data. It uses the correlation among the training samples to tackle the instability problem of sparse representation coding. Trace lasso naturally clusters the highly correlated data together. However, the face images with similar variations, such as illumination...
It is a great challenge for face recognition with single training sample per person. In this paper, we try to propose a new algorithm based sparse representation to solve this problem. The algorithm takes the two-dimensional training samples as the training set directly rather than image vectors. So we can obtain the dictionary of sparse representation only using one sample. The proposed algorithm...
Varying illumination conditions affect the appearance of face images significantly. Thus, it severely degrades image-based face recognition performance. This paper presents a novel face image pre-processing approach that deals with the illumination problem to make face recognition robust to illumination variations. In the proposed method, logarithm transform is first used to convert a face image into...
An efficient method is presented for determining multiple light sources illuminating faces using a general face depth map for comparable image generation. A 3D face depth map is artificially illuminated by known sources from several known directions and a set of samples from each resulting image is collected. These and corresponding samples from probed face image are used to find the light source...
Reconstruction of a 3D face from a single 2D face image can be substantially useful in many areas such as security, forensic, 3D animation, and motion capture. Most 3D face reconstruction techniques require multiple 2D face images taken at different views in order to estimate the depth of each face component which is one of the most crucial parameters in 3D face reconstruction. We propose a new method...
We discuss a statistical shape-from-shading framework for images of general and unknown illumination. To overcome arbitrary illumination, the framework makes use of the fact that general lighting can be expressed using low-order spherical harmonics for convex Lambertian objects. We cast the classical shape-from-shading equation as a Partial Least Squares (PLS) regression problem, which allows for...
Automatically synthesizing the facial sketches of a facial image is highly challenging since facial images typically exhibit a wide range of poses, expressions and scales, and have differing degrees of illumination and/or occlusion. When the facial sketches are to be synthesized in the unique sketching style of a particular artist, the problem becomes even more complex. This study develops an automatic...
Sparse representation for machine learning has been exploited in past years. Several sparse representation based classification algorithms have been developed for some applications, for example, face recognition. In this paper, we propose an improved sparse representation based classification algorithm. Firstly, for a discriminative representation, a non-negative constraint of sparse coefficient is...
In this paper, a novel face recognition algorithm named elastic block set reconstruction (EBSR) is proposed. In our method, the EBSR face is used to represent a set of training faces and to simulate different factors in a query image. An EBSR face is constructed by using the blocks from the training face images which best match to the blocks of the query image at the corresponding locations. The elastic...
We present a low resolution face recognition technique based on a special type of convolutional neural network which is trained to extract facial features from face images and project them onto a low-dimensional space. The network is trained to reconstruct a reference image chosen beforehand, and it has been applied in visible and infrared light. Since the learning phase is achieved separately for...
The past decade has witnessed a significant progress in biometric technologies, to a large degree, due to the availability of a wide variety of public databases that enable benchmark performance evaluations. In this paper, we describe a new database that includes: (i) rotating head videos of 259 subjects; (ii) 250 hand-drawn face sketches of 50 subjects. Rotating head videos were acquired under both...
We present a face reconstruction framework based on a statistical model that merges shape (2D and height maps), appearance (albedo), and spherical harmonics projection (SHP) information. The framework takes a 2D frontal face image under arbitrary illumination as input and outputs the estimated 3D shape and appearance. Face identification is performed using the shape and albedo coefficients of the...
We describe a face reconstruction framework based on a statistical model that combines shape (2D and height maps), appearance (albedo), and spherical harmonics projection (SHP) information. The framework takes a 2D frontal face image under arbitrary illumination as input and outputs the estimated 3D shape and appearance. Face identification is performed using the shape and albedo coefficients of the...
In many various applications facial images are dramatically changed especially by lighting variations, so that facial appearance changes caused serious performance degradation in face recognition. In this paper we describe a method to address illumination removal for face recognition using Empirical Mode Decomposition (EMD) to decompose subimages of Dual-Tree Complex Wavelet Transform (DT-CWT) into...
We propose a novel method to correct for arbitrary illumination variation in the face images. The main purpose is to improve recognition results of face images taken under uncontrolled illumination conditions. We correct the illumination variation in the face images using a face shape model, which allows us to estimate the face shape in the face image. Using this face shape, we can reconstruct a face...
We have investigated a technique for recognising faces invariant of facial expressions. We apply multi-linear tensor algebra, which subsumes linear algebra, to analyse and recognise 3D face surfaces. This potent framework possesses a remarkable ability to deal with the shortcomings of principle component analysis in less constrained situations. A set of vector spaces can be used to represent the variation...
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