A survey of approaches to three-dimensional face recognition

Bowyer, Kevin W.; Chang, Kai-Chun; Flynn, Patrick J.

doi:10.1109/icpr.2004.1334126

Cited by 104 publications

(60 citation statements)

References 14 publications

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“…For example, an image named yaleB * * P00A 005E * * .pgm is distributed into the subset 005E to denote 5 degrees azimuth. Each subset contains images with different elevation ranged in [0,5,10,15,20,25,35,50,60,70,85,95,110,120,130]. In this way, total 15 subsets in Table 5 have Table 5 The sample number of each subset divided by azimuth Subset 000E 005E 010E 015E 020E 025E 035E 050E 060E 070E 085E 095E 110E 120E 130E No.…”

Section: Compensation In Horizontal Directionmentioning

confidence: 99%

Illumination Compensation for Face Recognition Using Only One Image

Lin

Zhao

Tang

et al. 2013

Acta Automatica Sinica

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Section: Compensation In Horizontal Directionmentioning

confidence: 99%

Illumination Compensation for Face Recognition Using Only One Image

Lin

Zhao

Tang

et al. 2013

Acta Automatica Sinica

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“…Face recognition has received great attention in the past few years. A recent literature survey of 3D face recognition is given in [9]. Because our algorithm uses calibrated orthogonal cameras for capturing images, we cannot experiment on public databases, since there is no public database captured with available internal and external stereo camera parameters and setup similar to Fig.1.…”

Section: Application To 3d Face Recognitionmentioning

confidence: 99%

Disparity-Based 3D Face Modeling using 3D Deformable Facial Mask for 3D Face Recognition

Ansari

Abdel-Mottaleb

Mahoor

2006

2006 IEEE International Conference on Multimedia and Expo

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We present an automatic disparity-based approach for 3D face modeling, from two frontal and one profile view stereo images, for 3D face recognition applications. Once the images are captured, the algorithm starts by extracting selected 2D facial features from one of the frontal views and computes a dense disparity map from the two frontal images. Using the extracted 2D features plus their corresponding disparities in the disparity map, we compute their 3D coordinates. We next align a low resolution 3D mesh model to the 3D features, re-project it's vertices on the frontal 2D image and adjust its profile line vertices using the profile view. We increase the resolutions of the resulting 2D model only at its center region to obtain a facial mask model covering distinctive features of the face. The computation of the 2D vertices coordinates with their disparities results in a deformed 3D model mask specific to a give subject's face. Application of the model in 3D face recognition validates the algorithm and shows a high recognition rate.

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“…These systems have problems dealing with light variations [6]. Some of the proposed solutions use 3D facial recognition [1] and combine face recognition in both visible and IR spectrum [7].…”

Section: Introductionmentioning

confidence: 99%

Improving Face Segmentation in Thermograms Using Image Signatures

Filipe

Alexandre

2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. The aim of this paper is to present a method for the automatic segmentation of face images captured in Long Wavelength Infrared (LWIR), allowing for a large range of face rotations and expressions. The motivation behind this effort is to enable better performance of face recognition methods in the thermal Infrared (IR) images. The proposed method consists on the modelling of background and face pixels by two normal distributions each, followed by a post-processing step of face dilation for closing holes and delimitation based on vertical and horizontal images signatures. Our experiments were performed on images of the University of Notre Dame (UND) and Florida State University (FSU) databases. The obtained results improve on previous existing methods from 2.8% to more than 25% depending on the method and database.

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A survey of approaches to three-dimensional face recognition

Cited by 104 publications

References 14 publications

Illumination Compensation for Face Recognition Using Only One Image

Illumination Compensation for Face Recognition Using Only One Image

Disparity-Based 3D Face Modeling using 3D Deformable Facial Mask for 3D Face Recognition

Improving Face Segmentation in Thermograms Using Image Signatures

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