A Model Based Energy Minimization Method for 3D Face Reconstruction

Onofrio, D.; Tubaro, Stefano

doi:10.1109/icme.2005.1521661

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…Nikolova [20] explained the use of energy minimization methods in the field of image analysis and processing. Onofrio and Tubaro applied the same to the problem of three-dimensional (3D) face recognition [21]. Standard [22] explained the use of energy minimization to determine the states for a molecule in chemistry; he explained that the geometry of molecule is changed in a stepwise fashion so that the energy is reduced to lowest minimum.…”

Section: Energy Minimizationmentioning

confidence: 99%

Modeling and Simulation of Heat Transfer Phenomena

Rafique¹

2015

Heat Transfer Studies and Applications

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This chapter enlists, describes, explains and elaborates with examples these techniques as applied to problems and practical scenarios of heat transfer. 2. Basic techniques of modeling and simulation Modeling and simulation, like any other field of science and technology has some certain basic techniques using which all practices are carried out. These are the foundation stones on which the building of modeling and simulation practices and procedures is built.

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Section: Energy Minimizationmentioning

confidence: 99%

Modeling and Simulation of Heat Transfer Phenomena

Rafique¹

2015

Heat Transfer Studies and Applications

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“…Since the pioneering work of Parke [1], various image-based modeling techniques have been adopted to reconstruct 3D human faces during the past few years [2]. Stereo is a widely used technique for image-based modeling, but due to the smooth albedo across the facial skin, the human faces are lowly textured and the conventional stereo methods based on intensity correlation cannot give satisfying 3D face reconstruction results.…”

Section: Introductionmentioning

confidence: 99%

Morphable Face Reconstruction with Multiple Views

Wang

Liang

Zhang

2010

2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics

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The morphable modeling method of reconstructing 3D human face is based on a single given image, and the model fitting is ambiguous owing to the lack of depth information and geometry constraints between images. In this paper, we introduce a new face reconstruction method from multiple views based on the morphable model. The morphable face model is matched with multiple face images from different views. Using the multi-view geometry constraints, the ambiguity is efficiently eliminated and more realistic results are provided. We test our algorithm on the MPI face database and real images from our multi-camera system. The experimental results show the efficiency of the proposed method.

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“…The drawback of using a single 2D face image is the depth lost during 2D projection. Onofrio et al generated a 3D face model from multiple 2D images by using an energy minimization algorithm [4]. Wang et al proposed realistic 3D face reconstruction by fusing multiple 2D face images [5].…”

Section: Introductionmentioning

confidence: 99%

2D Face image depth ordering using adaptive Hillcrest-Valley classification and Otsu

Srichumroenrattana

Lursinsap

Lipikorn

2010

IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS

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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 that is capable of estimating the depth order of a 2D face image in various illumination conditions. The proposed method uses Hillcrest-Valley classification with adaptive mean filter and Otsu threshold value selection to estimate the depth order of an image. The proposed method was evaluated on the YaleB face database consists of 10 individual frontal faces for the total of 576 face images and the experimental results reveal that the proposed method is robust and it can estimate the depth order of a frontal face up to as many as 50 layers regardless of lighting condition.

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A Model Based Energy Minimization Method for 3D Face Reconstruction

Cited by 10 publications

References 5 publications

Modeling and Simulation of Heat Transfer Phenomena

Modeling and Simulation of Heat Transfer Phenomena

Morphable Face Reconstruction with Multiple Views

2D Face image depth ordering using adaptive Hillcrest-Valley classification and Otsu

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