Face Recognition - IEEE Conferences, Publications, and.

This paper summarizes the history and the most recent progresses in 3D face recognition research domain. The frontier research results are introduced in three categories: pose-invariant.

A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. This.

A Survey paper for Face Recognition Technologies.

Police Info- Communications Research Center (PICRC) attempts to evaluate the accuracy of face recognition technology by choosing some of the representative face recognition algorithms mentioned in MBE 2010 Still Face. PICRC has certain image database that stores two groups of full-faced photographs of people taken at intervals of 15 years. For instance as for the evaluation of the point (a.Research on 3D Face Recognition Algorithm. automatic face recognition has become the important part of the next generation computing technology. 3D face recognition methods are able to overcome the problems resulting from illumination, expression or pose variations in 2D face recognition. Facial feature mainly concentrate on the eyes, nose and mouth, therefore, this paper mainly detects the.A 3D Face Recognition Algorithm Using Histogram-based Features Xuebing Zhou 1,2 and Helmut Seibert 1,3 and Christoph Busch 2 and Wolfgang Funk2 1GRIS, TU Darmstadt 2 Fraunhofer IGD, Germany 3ZGDV e.V., Germany Abstract We present an automatic face recognition approach, which relies on the analysis of the three-dimensional facial surface. The proposed approach consists of two basic steps.


In order to improve the accuracy and speed of three-dimensional face recognition, this paper proposes a three-dimensional face recognition method combining LBP and SVM. First, the LBP algorithm is used to extract the feature information of the three-dimensional face depth image, then the SVM algorithm is used to classify the feature information.Face recognition is a popular research topic with a number of applications in several industrial sectors including security, surveillance, entertainment, virtual reality, and human- machine interaction. Both 2D images and 3D data can now be easily acquired and used Multimodal Approach for Face Recognition using 3D-2D Face Feature Fusion.

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This paper discusses 3D sensing and how NIR light is used for facial recognition systems. It explains the importance of measuring radiant intensity of NIR emissions and the challenges of obtaining accurate measurement to ensure the quality of facial recognition systems in devices such as smartphones, laptops, and automobiles. It introduces Radiant's integrated NIR Intensity Lens solution and.

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Abstract—In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability.

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View Face Recognition Research Papers on Academia.edu for free.

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This has led to renewed interest in face recognition using 3D models of human faces. One unexplored avenue of research on facial analysis is the potential of using 3D models to augment the performance of traditional 2D, appearance-based techniques. Our dataset is designed with two main goals in mind. First, we would like to make available accurate and complete 3D models of faces to researchers.

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Three-dimensional face recognition (3D face recognition) is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. It has been shown that 3D face recognition methods can achieve significantly higher accuracy than their 2D counterparts, rivaling fingerprint recognition.

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In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be.

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Moreover, 3D face recognition systems could accurately recognize human faces even under dim lights and with variant facial positions and expressions, in such conditions 2D face recognition systems would have immense difficulty to operate. This paper summarizes the history and the most recent progresses in 3D face recognition research domain. The frontier research results are introduced in.

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Capture a new 3D face database for testing within the project and for the benefit of the worldwide face recognition research community. Apply novel and existing state-of-the-art face recognition algorithms to the dataset. Capture skin reflectance data in order to generate synthetic poses of any face captured by the device. Project stages. Stages one to three were researched in partnership with.

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This paper proposes a novel approach to 3D Facial Expression Recognition (FER), and it is based on a Fast and Light Manifold CNN model, namely FLM-CNN. Different from current manifold CNNs, FLM-CNN adopts a human vision inspired pooling structure and a multi-scale encoding strategy to enhance geometry representation, which highlights shape characteristics of expressions and runs efficiently.

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