Uncontrolled Illumination Research Articles

A novel illumination-robust local descriptor based on sparse linear regression

Robust face recognition under uncontrolled illumination conditions is an important problem for real face recognition systems. In this paper, we introduce a novel illumination-robust local descriptor named Sparse Linear Regression Binary (SLRB) descriptor. The SLRB descriptor is a bit string by binarizing the sparse linear regression coefficients in a local block. It is an illumination-insensitive descriptor based on the locally linear consistency assumption under the Lambertian reflectance model. We use the cosine similarity and Hamming similarity as the similarity measure for the SLRB descriptor of two different images respectively. Experimental results on the Extended Yale-B and CMU-PIE face database show a promising performance compared to the existing representative approaches.

Two-view line matching algorithm based on context and appearance in low-textured images

A novel approach for line detection and matching is proposed, aimed at achieving good performance with low-textured scenes, under uncontrolled illumination conditions. Line detection is performed by means of phase-based edge detector over Gaussian scale-space, followed by a multi-scale fusion stage which has been proven to be profitable in minimizing the number of fragmented and overlapped segments. Line matching is performed by an iterative process that uses structural information collected through the use of different line neighborhoods, making the set of matched lines grow robustly at each iteration. Results show that this approach is suitable to deal with low-textured scenes, and also robust under a wide variety of image transformations.

Building volumetric appearance models of fabric using micro CT imaging

Cloth is essential to our everyday lives; consequently, visualizing and rendering cloth has been an important area of research in graphics for decades. One important aspect contributing to the rich appearance of cloth is its complex 3D structure. Volumetric algorithms that model this 3D structure can correctly simulate the interaction of light with cloth to produce highly realistic images of cloth. But creating volumetric models of cloth is difficult: writing specialized procedures for each type of material is onerous, and requires significant programmer effort and intuition. Further, the resulting models look unrealistically "perfect" because they lack visually important features like naturally occurring irregularities. This paper proposes a new approach to acquiring volume models, based on density data from X-ray computed tomography (CT) scans and appearance data from photographs under uncontrolled illumination. To model a material, a CT scan is made, yielding a scalar density volume. This 3D data has micron resolution details about the structure of cloth but lacks all optical information. So we combine this density data with a reference photograph of the cloth sample to infer its optical properties. We show that this approach can easily produce volume appearance models with extreme detail, and at larger scales the distinctive textures and highlights of a range of very different fabrics such as satin and velvet emerge automatically---all based simply on having accurate mesoscale geometry.<!-- END_PAGE_1 -->

Multilayer Surface Albedo for Face Recognition With Reference Images in Bad Lighting Conditions

In this paper, we propose a multilayer surface albedo (MLSA) model to tackle face recognition in bad lighting conditions, especially with reference images in bad lighting conditions. Some previous researches conclude that illumination variations mainly lie in the large-scale features of an image and extract small-scale features in the surface albedo (or surface texture). However, this surface albedo is not robust enough, which still contains some detrimental sharp features. To improve robustness of the surface albedo, MLSA further decomposes it as a linear sum of several detailed layers, to separate and represent features of different scales in a more specific way. Then, the layers are adjusted by separate weights, which are global parameters and selected for only once. A criterion function is developed to select these layer weights with an independent training set. Despite controlled illumination variations, MLSA is also effective to uncontrolled illumination variations, even mixed with other complicated variations (expression, pose, occlusion, and so on). Extensive experiments on four benchmark data sets show that MLSA has good receiver operating characteristic curve and statistical discriminating capability. The refined albedo improves recognition performance, especially with reference images in bad lighting conditions.

Robust Face Recognition from NIR Dataset via Sparse Representation

A biometric identification system may be a pc application for mechanically distinctive or confirmative of an individual from a digital image or a video frame from a video supply. One in all the ways that to try and do this can be by examination designated face expression from the image and a facial information. This paper planned dynamic face recognition from near-infrared images by exploitation sparse representation classifier. Most of the prevailing datasets for facial expressions are captured in a very visible light spectrum. However, the visible light (VIS) will modify with time and placement, causing important variations in look and texture. This new framework was designed to attain strength to pose variation and occlusion and to resolve uncontrolled environmental illumination for reliable biometric identification. This paper gift a unique analysis on a dynamic facial features recognition, exploitation near-infrared (NIR) datasets and LBP(Local binary patterns) feature descriptors. It shows sensible and strong results against illumination variations by exploitation infrared imaging system.

Comparison of Multi-View Face Recognition using DCT and Hybrid DWT of Score Fusion under Uncontrolled Illumination Variation

A facial recognition system is one of the biometric applications for automatically identifying or verifying a person from a digital image or a video frame from a video source. Now a day’s face recognition system is recognize the face using multiple-views of faces, for detecting each view of face such as left, right, front, top, and bottom. The proposed face recognition system consists of a novel illuminationinsensitive preprocessing method, a hybrid discrete Wavelet Transform (HDWT) for feature extraction and Density based Score Fusion Technique for matching. First, in the preprocessing stage different stages are followed like Gamma Correction, DOG(Difference of Gaussion)filtering and contrast equalization which transforms, a face image into an illumination-insensitive image, Then, for feature extraction of complementary classifiers, multiple face models based upon HDWT are applied. To do the feature extraction this system uses DCT Wavelet transform to generate the feature vectors of the query and database images. Euclidean distance is used as similarity measure to compare the image. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system uses the ORL face database, and compare with DCT method. Using ORL database the Hybrid wavelet has achieves smaller size feature vectors which is performing better with 98.55% accuracy and less computation time require for recognizing image which is 19.0645 msec to 24.7653 msec .Which is less as compare to DCT method, DCT method achieves 96.45% accuracy and computational time 26.2349 msec to 33.0234 msec which is more as compare to proposed method..

Generalized Weber-face for illumination-robust face recognition

Robust face recognition under uncontrolled illumination conditions is one of the key challenges for real-time face recognition systems. Weber-face (WF) is an illumination insensitive face representation based on Weber׳ law. In this letter, we develop a generalized Weber-face (GWF) which extracts the statistics of multi-scale information from face images. By assigning different weights to the inner-ground and outer-ground we further develop a weighted GWF (wGWF) version. Based on our experiments on the extended Yale-B and FERET face database we show that the proposed methods are robust to illumination variations and can obtain promising performance comparable with existing approaches.

On-set performance capture of multiple actors with a stereo camera

State-of-the-art marker-less performance capture algorithms reconstruct detailed human skeletal motion and space-time coherent surface geometry. Despite being a big improvement over marker-based motion capture methods, they are still rarely applied in practical VFX productions as they require ten or more cameras and a studio with controlled lighting or a green screen background. If one was able to capture performances directly on a general set using only the primary stereo camera used for principal photography, many possibilities would open up in virtual production and previsualization, the creation of virtual actors, and video editing during post-production. We describe a new algorithm which works towards this goal. It is able to track skeletal motion and detailed surface geometry of one or more actors from footage recorded with a stereo rig that is allowed to move. It succeeds in general sets with uncontrolled background and uncontrolled illumination, and scenes in which actors strike non-frontal poses. It is one of the first performance capture methods to exploit detailed BRDF information and scene illumination for accurate pose tracking and surface refinement in general scenes. It also relies on a new foreground segmentation approach that combines appearance, stereo, and pose tracking results to segment out actors from the background. Appearance, segmentation, and motion cues are combined in a new pose optimization framework that is robust under uncontrolled lighting, uncontrolled background and very sparse camera views.

A biologically inspired scale-space for illumination invariant feature detection

This paper presents a new illumination invariant operator, combining the nonlinear characteristics of biological center-surround cells with the classic difference of Gaussians operator. It specifically targets the underexposed image regions, exhibiting increased sensitivity to low contrast, while not affecting performance in the correctly exposed ones. The proposed operator can be used to create a scale-space, which in turn can be a part of a SIFT-based detector module. The main advantage of this illumination invariant scale-space is that, using just one global threshold, keypoints can be detected in both dark and bright image regions. In order to evaluate the degree of illumination invariance that the proposed, as well as other, existing, operators exhibit, a new benchmark dataset is introduced. It features a greater variety of imaging conditions, compared to existing databases, containing real scenes under various degrees and combinations of uniform and non-uniform illumination. Experimental results show that the proposed detector extracts a greater number of features, with a high level of repeatability, compared to other approaches, for both uniform and non-uniform illumination. This, along with its simple implementation, renders the proposed feature detector particularly appropriate for outdoor vision systems, working in environments under uncontrolled illumination conditions.

Multiscale Local Phase Quantization for Robust Component-Based Face Recognition Using Kernel Fusion of Multiple Descriptors

Face recognition subject to uncontrolled illumination and blur is challenging. Interestingly, image degradation caused by blurring, often present in real-world imagery, has mostly been overlooked by the face recognition community. Such degradation corrupts face information and affects image alignment, which together negatively impact recognition accuracy. We propose a number of countermeasures designed to achieve system robustness to blurring. First, we propose a novel blur-robust face image descriptor based on Local Phase Quantization (LPQ) and extend it to a multiscale framework (MLPQ) to increase its effectiveness. To maximize the insensitivity to misalignment, the MLPQ descriptor is computed regionally by adopting a component-based framework. Second, the regional features are combined using kernel fusion. Third, the proposed MLPQ representation is combined with the Multiscale Local Binary Pattern (MLBP) descriptor using kernel fusion to increase insensitivity to illumination. Kernel Discriminant Analysis (KDA) of the combined features extracts discriminative information for face recognition. Last, two geometric normalizations are used to generate and combine multiple scores from different face image scales to further enhance the accuracy. The proposed approach has been comprehensively evaluated using the combined Yale and Extended Yale database B (degraded by artificially induced linear motion blur) as well as the FERET, FRGC 2.0, and LFW databases. The combined system is comparable to state-of-the-art approaches using similar system configurations. The reported work provides a new insight into the merits of various face representation and fusion methods, as well as their role in dealing with variable lighting and blur degradation.

Camera Sensor Arrangement for Crop/Weed Detection Accuracy in Agronomic Images

In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes. Accuracy of identification and detection is an important issue to be addressed in image processing. There are two main types of parameters affecting the accuracy of the images, namely: (a) extrinsic, related to the sensor's positioning in the tractor; (b) intrinsic, related to the sensor specifications, such as CCD resolution, focal length or iris aperture, among others. Moreover, in agricultural applications, the uncontrolled illumination, existing in outdoor environments, is also an important factor affecting the image accuracy. This paper is exclusively focused on two main issues, always with the goal to achieve the highest image accuracy in Precision Agriculture applications, making the following two main contributions: (a) camera sensor arrangement, to adjust extrinsic parameters and (b) design of strategies for controlling the adverse illumination effects.

Low-Resolution Face Tracker Robust to Illumination Variations

In many practical video surveillance applications, the faces acquired by outdoor cameras are of low resolution and are affected by uncontrolled illumination. Although significant efforts have been made to facilitate face tracking or illumination normalization in unconstrained videos, the approaches developed may not be effective in video surveillance applications. This is because: 1) a low-resolution face contains limited information, and 2) major changes in illumination on a small region of the face make the tracking ineffective. To overcome this problem, this paper proposes to perform tracking in an illumination-insensitive feature space, called the gradient logarithm field (GLF) feature space. The GLF feature mainly depends on the intrinsic characteristics of a face and is only marginally affected by the lighting source. In addition, the GLF feature is a global feature and does not depend on a specific face model, and thus is effective in tracking low-resolution faces. Experimental results show that the proposed GLF-based tracker works well under significant illumination changes and outperforms many state-of-the-art tracking algorithms.

Comparing face recognition algorithms to humans on challenging tasks

We compared face identification by humans and machines using images taken under a variety of uncontrolled illumination conditions in both indoor and outdoor settings. Natural variations in a person's day-to-day appearance (e.g., hair style, facial expression, hats, glasses, etc.) contributed to the difficulty of the task. Both humans and machines matched the identity of people (same or different) in pairs of frontal view face images. The degree of difficulty introduced by photometric and appearance-based variability was estimated using a face recognition algorithm created by fusing three top-performing algorithms from a recent international competition. The algorithm computed similarity scores for a constant set of same-identity and different-identity pairings from multiple images. Image pairs were assigned to good , moderate , and poor accuracy groups by ranking the similarity scores for each identity pairing, and dividing these rankings into three strata. This procedure isolated the role of photometric variables from the effects of the distinctiveness of particular identities. Algorithm performance for these constant identity pairings varied dramatically across the groups. In a series of experiments, humans matched image pairs from the good, moderate, and poor conditions, rating the likelihood that the images were of the same person (1: sure same - 5: sure different). Algorithms were more accurate than humans in the good and moderate conditions, but were comparable to humans in the poor accuracy condition. To date, these are the most variable illumination- and appearance-based recognition conditions on which humans and machines have been compared. The finding that machines were never less accurate than humans on these challenging frontal images suggests that face recognition systems may be ready for applications with comparable difficulty. We speculate that the superiority of algorithms over humans in the less challenging conditions may be due to the algorithms' use of detailed, view-specific identity information. Humans may consider this information less important due to its limited potential for robust generalization in suboptimal viewing conditions.

License Plate Detection and Segmentation Using Cluster Run Length Smoothing Algorithm

For the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate detection system is proposed for Malaysian vehicles with standard license plates based on image processing and clustering. Detecting the location of license plate is a vital issue when dealing with uncontrolled environments and illumination difficulty. Therefore, a proposed algorithm called Cluster Run Length Smoothing Algorithm (CRLSA) was applied to locate the license plates at the right position. CRLSA consisted of two separate proposed algorithms which applied run length edge detector algorithm using kernel masks and 128 grayscale offset plus a three-dimensional way to calculate run length smoothing algorithm, which can improve clustering techniques in segmentation phase. Six separate experiments were performed; Morphology, CRLSA, Clustering, Square/Contour Detection, Hough, and Radon Transform. From those experiments, analysis based on segmentation errors was constructed. The prototyped system has accuracy more than 96%.

Several Face Images Recognition in Uncontrolled Illumination Variation Images

This paper to study and present a face recognition system under uncontrolled illumination images. It consists of pre-processing method, a hybrid Fourier feature extraction and a score fusion. In the pre-processing stage a face image is transformed in to an illumination-insensitive image, called an “integral normalized gradient image”. The hybrids Fourier feature are extracted from different Fourier domains in different frequency bandwidth. In this paper the multiple Face Models are generated by plural normalized face images that have different eye distances. Finally to combine the scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. This system using the Face recognition Grand Challenge (FRGC) experimental protocols is evaluated. it shows the average verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.

Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation

Many classic and contemporary face recognition algorithms work well on public data sets, but degrade sharply when they are used in a real recognition system. This is mostly due to the difficulty of simultaneously handling variations in illumination, image misalignment, and occlusion in the test image. We consider a scenario where the training images are well controlled and test images are only loosely controlled. We propose a conceptually simple face recognition system that achieves a high degree of robustness and stability to illumination variation, image misalignment, and partial occlusion. The system uses tools from sparse representation to align a test face image to a set of frontal training images. The region of attraction of our alignment algorithm is computed empirically for public face data sets such as Multi-PIE. We demonstrate how to capture a set of training images with enough illumination variation that they span test images taken under uncontrolled illumination. In order to evaluate how our algorithms work under practical testing conditions, we have implemented a complete face recognition system, including a projector-based training acquisition system. Our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.

Photographing Paintings by Image Fusion

This paper addresses the problem of obtaining a quality photograph of a painting by multi-image fusion methods. The problem is particularly challenging because of the uncontrolled illumination conditions and of the destructive reflection speckle present in most photographs of paintings. A fully automatic image processing chain is described that, starting from several bursts of a painting taken under different angles, permits one to obtain the best possible result by eliminating highlights and motion blur by robust statistics, reducing noise by fusion, and compensating optical distortion in the registration process. This image fusion method is applicable to photographs of a painting taken with a hand-held camera without any particular setup. It works under bad lighting conditions and eliminates motion blur, even when the painting is protected by a glass screen creating structured reflections of the room. The careful discussion of each step of the processing chain also permits one to review and discuss the efficiency of the image fusion tools recently proposed in the literature and insert several new ones in the chain.

Building volumetric appearance models of fabric using micro CT imaging

The appearance of complex, thick materials like textiles is determined by their 3D structure, and they are incompletely described by surface reflection models alone. While volume scattering can produce highly realistic images of such materials, creating the required volume density models is difficult. Procedural approaches require significant programmer effort and intuition to design specialpurpose algorithms for each material. Further, the resulting models lack the visual complexity of real materials with their naturally-arising irregularities. This paper proposes a new approach to acquiring volume models, based on density data from X-ray computed tomography (CT) scans and appearance data from photographs under uncontrolled illumination. To model a material, a CT scan is made, resulting in a scalar density volume. This 3D data is processed to extract orientation information and remove noise. The resulting density and orientation fields are used in an appearance matching procedure to define scattering properties in the volume that, when rendered, produce images with texture statistics that match the photographs. As our results show, this approach can easily produce volume appearance models with extreme detail, and at larger scales the distinctive textures and highlights of a range of very different fabrics like satin and velvet emerge automatically---all based simply on having accurate mesoscale geometry.

Face recognition based on improved Retinex and sparse representation

In this paper, we proposed a method based on improved Retinex theory and sparse representation to deal with the difficulties for face recognition under inhomogeneous illumination. In our work, the total variation model was introduced to optimize the parameters of Retinex and the illumination insensitive features were extracted as the dictionary of sparse representation. Finally, the facial images could be recognized by the proposed algorithm. The experimental results on different benchmark face databases indicated that the proposed approach could be more efficient than traditional methods for face images under uncontrolled illumination conditions.

Boosting Color Feature Selection for Color Face Recognition

This paper introduces the new color face recognition (FR) method that makes effective use of boosting learning as color-component feature selection framework. The proposed boosting color-component feature selection framework is designed for finding the best set of color-component features from various color spaces (or models), aiming to achieve the best FR performance for a given FR task. In addition, to facilitate the complementary effect of the selected color-component features for the purpose of color FR, they are combined using the proposed weighted feature fusion scheme. The effectiveness of our color FR method has been successfully evaluated on the following five public face databases (DBs): CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that the results of the proposed method are impressively better than the results of other state-of-the-art color FR methods over different FR challenges including highly uncontrolled illumination, moderate pose variation, and small resolution face images.