Results 1 - 10
of
67
Illumination Invariant Face Recognition Using Near-Infrared Images
"... Abstract—Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thusconstrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a nove ..."
Abstract
-
Cited by 81 (12 self)
- Add to MetaCart
(Show Context)
Abstract—Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thusconstrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR imagebased face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups.
Multispectral Imaging Using Multiplexed Illumination
"... Many vision tasks such as scene segmentation, or the recognition of materials within a scene, become considerably easier when it is possible to measure the spectral reflectance of scene surfaces. In this paper, we present an efficient and robust approach for recovering spectral reflectance in a scen ..."
Abstract
-
Cited by 49 (3 self)
- Add to MetaCart
(Show Context)
Many vision tasks such as scene segmentation, or the recognition of materials within a scene, become considerably easier when it is possible to measure the spectral reflectance of scene surfaces. In this paper, we present an efficient and robust approach for recovering spectral reflectance in a scene that combines the advantages of using multiple spectral sources and a multispectral camera. We have implemented a system based on this approach using a cluster of light sources with different spectra to illuminate the scene and a conventional RGB camera to acquire images. Rather than sequentially activating the sources, we have developed a novel technique to determine the optimal multiplexing sequence of spectral sources so as to minimize the number of acquired images. We use our recovered spectral measurements to recover the continuous spectral reflectance for each scene point by using a linear model for spectral reflectance. Our imaging system can produce multispectral videos of scenes at 30fps. We demonstrate the effectiveness of our system through extensive evaluation. As a demonstration, we present the results of applying data recovered by our system to material segmentation and spectral relighting. 1.
A Survey of 3D Face Recognition Methods
- In Lecture Notes in Computer Science
, 2005
"... Abstract. Many researches in face recognition have been dealing with the challenge of the great variability in head pose, lighting intensity and direction,facial expression, and aging. The main purpose of this overview is to describe the recent 3D face recognition algorithms. The last few years more ..."
Abstract
-
Cited by 29 (2 self)
- Add to MetaCart
Abstract. Many researches in face recognition have been dealing with the challenge of the great variability in head pose, lighting intensity and direction,facial expression, and aging. The main purpose of this overview is to describe the recent 3D face recognition algorithms. The last few years more and more 2D face recognition algorithms are improved and tested on less than perfect images. However, 3D models hold more information of the face, like surface information, that can be used for face recognition or subject discrimination. Another major advantage is that 3D face recognition is pose invariant. A disadvantage of most presented 3D face recognition methods is that they still treat the human face as a rigid object. This means that the methods aren’t capable of handling facial expressions. Although 2D face recognition still seems to outperform the 3D face recognition methods, it is expected that this will change in the near future. 1
Multispectral Iris Analysis: A Preliminary Study
"... This paper explores the possibility of using multispectral iris information to enhance the recognition performance of an iris biometric system. Commercial iris recognition systems typically sense the iridal reflection pertaining to the near-infrared (IR) range of the electromagnetic spectrum. This w ..."
Abstract
-
Cited by 19 (7 self)
- Add to MetaCart
(Show Context)
This paper explores the possibility of using multispectral iris information to enhance the recognition performance of an iris biometric system. Commercial iris recognition systems typically sense the iridal reflection pertaining to the near-infrared (IR) range of the electromagnetic spectrum. This work examines the iris information represented in the visible and IR portion of the spectrum. It is hypothesized that, based on the color of the eye, different components of the iris are highlighted at multiple wavelengths. To this end, an acquisition procedure for obtaining co-registered multispectral iris images associated with the IR, Red, Green and Blue wavelengths of the electromagnetic spectrum, is first discussed. The components of the iris that are revealed in multiple spectral channels/wavelengths based on the color of the eye are studied. An adaptive histogram equalization scheme is invoked to enhance the iris structure. The performance of iris recognition across multiple wavelengths is next evaluated. Experiments indicate the potential of
Highly accurate and fast face recognition using near infrared images
- in Proc. IAPR Int. Conf. Biometrics
, 2006
"... Abstract. In this paper, we present a highly accurate, realtime face recognition system for cooperative user applications. The novelties are: (1) a novel design of camera hardware, and (2) a learning based procedure for effective face and eye detection and recognition with the resulting imagery. The ..."
Abstract
-
Cited by 12 (3 self)
- Add to MetaCart
(Show Context)
Abstract. In this paper, we present a highly accurate, realtime face recognition system for cooperative user applications. The novelties are: (1) a novel design of camera hardware, and (2) a learning based procedure for effective face and eye detection and recognition with the resulting imagery. The hardware minimizes environmental lighting and delivers face images with frontal lighting. This avoids many problems in subsequent face processing to a great extent. The face detection and recognition algorithms are based on a local feature representation. Statistical learning is applied to learn most effective features and classifiers for building face detection and recognition engines. The novel imaging system and the detection and recognition engines are integrated into a powerful face recognition system. Evaluated in real-world user scenario, a condition that is harder than a technology evaluation such as Face Recognition Vendor Tests (FRVT), the system has demonstrated excellent accuracy, speed and usability. 1
Multispectral Visible and Infrared Imaging for Face Recognition
"... Multispectral imaging in the visible and near infrared spectra helps reduce color variations in the face due to changes in illumination source types and directions. Thermal infrared imaging provides useful signatures of the face that is insensitive to ambient lighting through the measurement of heat ..."
Abstract
-
Cited by 12 (2 self)
- Add to MetaCart
(Show Context)
Multispectral imaging in the visible and near infrared spectra helps reduce color variations in the face due to changes in illumination source types and directions. Thermal infrared imaging provides useful signatures of the face that is insensitive to ambient lighting through the measurement of heat energy radiated from the object. This paper introduces the use of multispectral imaging and thermal infrared imaging as alternative means to conventional broadband monochrome or color imaging sensors in order to enhance the performance of face recognition in uncontrolled illumination conditions. 1.
M.: An indoor and outdoor, multimodal, multispectral and multiilluminant database for face recognition
- Proc. IEEE CVPR Workshop Multi-model Biom. 54–61
, 2006
"... In this effort, we describe a face database obtained by using an involved acquisition system for the collection of multimodal and multispectral image data under various illumination conditions. The database of facial images may aid in exploring new avenues in face recognition, especially when involv ..."
Abstract
-
Cited by 11 (5 self)
- Add to MetaCart
(Show Context)
In this effort, we describe a face database obtained by using an involved acquisition system for the collection of multimodal and multispectral image data under various illumination conditions. The database of facial images may aid in exploring new avenues in face recognition, especially when involving multi-band visible and thermal information. The database has applications in experimentation with human identification, authentication, and face modeling. We describe the abilities of the acquisition system wherein the imaging sensors, system considerations and registration are discussed. For each data record in the database, we have twenty five images spanning the visible spectrum, one visible RGB/monochromatic image, one thermal image and the spectral distribution of the illumination of the scene. The complete dataset has so far 2624 face images and the corresponding illumination information for each data record. Moreover, we present a multispectral data fusion approach for illumination adjustment. The database will be made public to the research community for perusal towards illumination invariant face related research. We acquired a face database with visible, thermal, and multispectral images (in the visible spectrum, 0.4 µm to 0.72 µm) under different illumination conditions, including halogen, fluorescent, and day light. In Figure 1, we show one data record in our database. The visible RGB image acquired under a fluorescent illuminant is
Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image
- IEEE Trans. Image Process
, 2006
"... Abstract—In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is pro-posed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly non-linear mapping kernel PCA (DKPCA) is proposed to per ..."
Abstract
-
Cited by 8 (1 self)
- Add to MetaCart
(Show Context)
Abstract—In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is pro-posed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly non-linear mapping kernel PCA (DKPCA) is proposed to perform fea-ture transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional fea-ture space in order to make the mapped features linearly separable. However, this method does not consider the structural character-istics of the face images, and it is difficult to determine which non-linear mapping is more effective for face recognition. In this paper, a new method of nonlinear mapping, which is performed in the original feature space, is defined. The proposed nonlinear mapping not only considers the statistical property of the input features, but also adopts an eigenmask to emphasize those important facial feature points. Therefore, after this mapping, the transformed fea-tures have a higher discriminating power, and the relative impor-tance of the features adapts to the spatial importance of the face images. This new nonlinear mapping is combined with the conven-tional kernel PCA to be called “doubly ” nonlinear mapping kernel PCA. The proposed algorithm is evaluated based on the Yale data-base, the AR database, the ORL database and the YaleB database by using different face recognition methods such as PCA, Gabor wavelets plus PCA, and Gabor wavelets plus kernel PCA with frac-tional power polynomial models. Experiments show that consistent and promising results are obtained. Index Terms—Doubly nonlinear mapping, face recognition, Gabor wavelets, kernel principal component analysis (KPCA). I.
Physics-based fusion of multispectral data for improved face recognition
- ICPR
"... A novel physics-based fusion of multispectral images within the visual spectra is proposed for the purpose of improving face recognition under constant or varying illumination. Spectral images are fused according to the physics properties of the imaging system, including illumination, spectral respo ..."
Abstract
-
Cited by 7 (6 self)
- Add to MetaCart
(Show Context)
A novel physics-based fusion of multispectral images within the visual spectra is proposed for the purpose of improving face recognition under constant or varying illumination. Spectral images are fused according to the physics properties of the imaging system, including illumination, spectral response of the camera, and spectral reflectance of skin. The fused image is given as a probe to the recognition software FaceIt ® which compares it to a gallery of images. The identification performance of our physics-based fusion method is compared to the performance of Principle Component Analysis and average fusion methods. The results show that the proposed fusion yields a higher identification rate. A method of illumination adjustment is proposed when the probe and gallery images are acquired under different illumination conditions. The results show that the identification rate is higher than that of unadjusted gray-level images. 1.
