Face verification from 3D and grey level clues
TL;DR: In this article, the 3D and grey level comparison algorithms were designed to be integrated in security applications in which individuals cooperate, and the residual error after 3D matching was used as a first similarity measure.
Abstract: We address in this paper automatic face verification from 3D facial surface and grey level analysis. 3D acquisition is performed by a structured light system, adapted to face capture and allowing grey level acquisition in alignment. The 3D facial shapes are compared and the residual error after 3D matching is used as a first similarity measure. A second similarity measure is derived from grey level comparison. As expected, fusing 3D and intensity information increases verification performances. The acquisition system, the 3D and grey level comparison algorithms were designed to be integrated in security applications in which individuals cooperate.
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TL;DR: This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images.
Abstract: This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of more accurate three-dimensional face recognition are identified. These challenges include the need for better sensors, improved recognition algorithms, and more rigorous experimental methodology.
1,069 citations
23 Dec 2008
TL;DR: A new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions is presented, which can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis.
Abstract: A new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions is presented in this paper. This database is unique from three aspects: i) the facial expressions are composed of judiciously selected subset of Action Units as well as the six basic emotions, and many actors/actresses are incorporated to obtain more realistic expression data; ii) a rich set of head pose variations are available; and iii) different types of face occlusions are included. Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis.
819 citations
TL;DR: A discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has been provided.
Abstract: Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.
751 citations
TL;DR: This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional images in the visual and infrared (IR) spectra.
Abstract: Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.
650 citations
TL;DR: The largest experimental study to date in multimodal 2D+3D face recognition, involving 198 persons in the gallery and either 198 or 670 time-lapse probe images, reaches major conclusions.
Abstract: We report on the largest experimental study to date in multimodal 2D+3D face recognition, involving 198 persons in the gallery and either 198 or 670 time-lapse probe images. PCA-based methods are used separately for each modality and match scores in the separate face spaces are combined for multimodal recognition. Major conclusions are: 1) 2D and 3D have similar recognition performance when considered individually, 2) combining 2D and 3D results using a simple weighting scheme outperforms either 2D or 3D alone, 3) combining results from two or more 2D images using a similar weighting scheme also outperforms a single 2D image, and 4) combined 2D+3D outperforms the multi-image 2D result. This is the first (so far, only) work to present such an experimental control to substantiate multimodal performance improvement.
470 citations
References
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01 May 1995
TL;DR: A critical survey of existing literature on human and machine recognition of faces is presented, followed by a brief overview of the literature on face recognition in the psychophysics community and a detailed overview of move than 20 years of research done in the engineering community.
Abstract: The goal of this paper is to present a critical survey of existing literature on human and machine recognition of faces. Machine recognition of faces has several applications, ranging from static matching of controlled photographs as in mug shots matching and credit card verification to surveillance video images. Such applications have different constraints in terms of complexity of processing requirements and thus present a wide range of different technical challenges. Over the last 20 years researchers in psychophysics, neural sciences and engineering, image processing analysis and computer vision have investigated a number of issues related to face recognition by humans and machines. Ongoing research activities have been given a renewed emphasis over the last five years. Existing techniques and systems have been tested on different sets of images of varying complexities. But very little synergism exists between studies in psychophysics and the engineering literature. Most importantly, there exists no evaluation or benchmarking studies using large databases with the image quality that arises in commercial and law enforcement applications In this paper, we first present different applications of face recognition in commercial and law enforcement sectors. This is followed by a brief overview of the literature on face recognition in the psychophysics community. We then present a detailed overview of move than 20 years of research done in the engineering community. Techniques for segmentation/location of the face, feature extraction and recognition are reviewed. Global transform and feature based methods using statistical, structural and neural classifiers are summarized. >
2,727 citations
TL;DR: A novel technique for the integration of multiple classifiers at an hybrid rank/measurement level is introduced using HyperBF networks and two different methods for the rejection of an unknown person are introduced.
Abstract: This paper presents a person identification system based on acoustic and visual features. The system is organized as a set of non-homogeneous classifiers whose outputs are integrated after a normalization step. In particular, two classifiers based on acoustic features and three based on visual ones provide data for an integration module whose performance is evaluated. A novel technique for the integration of multiple classifiers at an hybrid rank/measurement level is introduced using HyperBF networks. Two different methods for the rejection of an unknown person are introduced. The performance of the integrated system is shown to be superior to that of the acoustic and visual subsystems. The resulting identification system can be used to log personal access and, with minor modifications, as an identity verification system. >
663 citations
TL;DR: In this article, the authors explore the representation of the human face by features based on the curvature of the face surface, such as the shape of the forehead, jawline, and cheeks, which are not easily detected from standard intensity images.
Abstract: This paper explores the representation of the human face by features based on the curvature of the face surface. Curature captures many features necessary to accurately describe the face, such as the shape of the forehead, jawline, and cheeks, which are not easily detected from standard intensity images. Moreover, the value of curvature at a point on the surface is also viewpoint invariant. Until recently range data of high enough resolution and accuracy to perform useful curvature calculations on the scale of the human face had been unavailable. Although several researchers have worked on the problem of interpreting range data from curved (although usually highly geometrically structured) surfaces, the main approaches have centered on segmentation by signs of mean and Gaussian curvature which have not proved sufficient in themselves for the case of the human face. This paper details the calculation of principal curvature for a particular data set, the calculation of general surface descriptors based on curvature, and the calculation of face specific descriptors based both on curvature features and a priori knowledge about the structure of the face. These face specific descriptors can be incorporated into many different recognition strategies. A system that implements one such strategy, depth template comparison, giving recognition rates between 80% and 90% is described.
209 citations
TL;DR: The method exploits information which is complementary to gray level based approaches, enabling the fusion with those techniques, and is cheap and fast while offering a sufficient resolution for face recognition purposes.
Abstract: This paper presents automatic face authentication from facial surface analysis. This geometrical approach was motivated by difficulties encountered when considering frontal face recognition. Apart from being less sensitive to viewpoint and lighting conditions, the method exploits information which is complementary to gray level based approaches, enabling the fusion with those techniques. A 3D acquisition system based on structured light and adapted to facial surface capture is presented. It is cheap and fast while offering a sufficient resolution for face recognition purposes. The acquisition system and the 3D face comparison algorithm were designed to be integrated in security applications with cooperative scenario.
179 citations
10 Sep 1997
TL;DR: A system for face recognition using range images as input data is described, and two approaches, known from face recognition based on grey level images have been extended to dealing with range images.
Abstract: A system for face recognition using range images as input data is described. The range data acquisition procedure is based on the coded light approach, merging range images that are recorded by two separate sensors. Two approaches, which are known from face recognition based on grey level images have been extended to dealing with range images. These approaches are based on eigenfaces and hidden Markov models, respectively. Experimental results on a database with various range images from 24 persons show very promising results for both recognition methods.
119 citations