Face recognition in 2D and 2.5D using ridgelets and photometric stereo

Kautkar, Satyajit N.; Atkinson, Gary A.; Mj, Smith

doi:10.1016/j.patcog.2012.03.007

Cited by 26 publications

(8 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An unconstrained photometric-stereo method for estimating the surface normal and the surface reflectance of objects without a priori knowledge of the light source directions was proposed in [7]. Also, worth mentioning the work in [16,9,29] that are based on similar approaches. Recently approaches for 3D reconstruction based on photometric stereo with unknown lighting were proposed [4,25,1,26,31,22,21,27].…”

Section: Previous Workmentioning

confidence: 99%

Photometric Stereo for 3D Face Reconstruction Using Non Linear Illumination Models

Villarini

Gkelias

Argyriou³

2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Face recognition in presence of illumination changes, variant pose and different facial expressions is a challenging problem. In this paper, a method for 3D face reconstruction using photometric stereo and without knowing the illumination directions and facial expression is proposed in order to achieve improvement in face recognition. A dimensionality reduction method was introduced to represent the face deformations due to illumination variations and self shadows in a lower space. The obtained mapping function was used to determine the illumination direction of each input image and that direction was used to apply photometric stereo. Experiments with faces were performed in order to evaluate the performance of the proposed scheme. From the experiments it was shown that the proposed approach results very accurate 3D surfaces without knowing the light directions and with a very small differences compared to the case of known directions. As a result the proposed approach is more general and requires less restrictions enabling 3D face recognition methods to operate with less data.

show abstract

Section: Previous Workmentioning

confidence: 99%

Photometric Stereo for 3D Face Reconstruction Using Non Linear Illumination Models

Villarini

Gkelias

Argyriou³

2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…However, real-world situations of face image acquisition imply the use of different photographic hardware, including one or several cameras which could be omnidirectional or pan-tilt-zoom [25], and which could include, e.g. wide-field sensors [25], photometric stereo [26], etc. Cameras could work in the range of the visible light or use infra-red sensors, leading to multiple modalities for AFR [6].…”

Section: Image Resolution and Modalitymentioning

confidence: 99%

Automated Face Recognition: Challenges and Solutions

Olszewska¹

2016

Pattern Recognition - Analysis and Applications

View full text Add to dashboard Cite

Automated face recognition (AFR) aims to identify people in images or videos using pattern recognition techniques. Automated face recognition is widely used in applications ranging from social media to advanced authentication systems. Whilst techniques for face recognition are well established, the automatic recognition of faces captured by digital cameras in unconstrained, real-world environment is still very challenging, since it involves important variations in both acquisition conditions as well as in facial expressions and in pose changes. Thus, this chapter introduces the topic of computer automated face recognition in light of the main challenges in that research field and the developed solutions and applications based on image processing and artificial intelligence methods.

show abstract

“…This database containing 3187 sessions of 453 subjects captured in two sessions of duration of six months each, is one of the largest currently available databases [4]. The complete description about data capturing device and its setup is given in [5]. The added advantage of this database would be the manual allocation of eleven facial landmarks which makes alignment process simple.…”

Section: The Photoface 3d Databasementioning

confidence: 99%

Qualitative study on 3D face databases: A review

Dharavath

Laskar

Talukdar

2013

2013 Annual IEEE India Conference (INDICON)

View full text Add to dashboard Cite

Recent researches in three-dimensional (3D) face recognition area have proved that, 3D face recognition methods, achieve better accuracy than its 2D counterpart. One of the main advantages of 3D face recognition methods is that it measures the geometry of rigid features on the human face, due to which it becomes an invariant to illumination, expressions and rotations of head (pose variation). But the main drawback of 3D face recognition methods is the acquisition of 3D face images which generally needs a range camera. It is where the research community is facing difficulty. Research in 3D face recognition also depends on available databases. In this paper, we present a qualitative review of all the available 3D face databases. A comparative table of all the databases is also given at the end with all the necessary parameters.

show abstract

Face recognition in 2D and 2.5D using ridgelets and photometric stereo

Cited by 26 publications

References 54 publications

Photometric Stereo for 3D Face Reconstruction Using Non Linear Illumination Models

Photometric Stereo for 3D Face Reconstruction Using Non Linear Illumination Models

Automated Face Recognition: Challenges and Solutions

Qualitative study on 3D face databases: A review

Contact Info

Product

Resources

About