3D face detection using curvature analysis

Colombo, Alessandro; Cusano, Claudio; Schettini, Raimondo

doi:10.1016/j.patcog.2005.09.009

Cited by 193 publications

(96 citation statements)

References 7 publications

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“…Face detection in 3D images is not a common task; a primal approach has been presented in (Colombo et al, 2006). Detecting faces in 3D images presents some benefits: first of all, 3D data is independent from scale.…”

Section: Face Detection and Normalizationmentioning

confidence: 99%

“…The face detector is based on the work presented in (Colombo et al, 2006). The input of the algorithm is supposed to be a single 3D image of the scene.…”

Section: Algorithm Overviewmentioning

confidence: 99%

“…Using simple thresholding of low curvature values it is possible to isolate potential noses (convex regions) and eyes (elliptical concave regions). See (Colombo et al, 2006) for an in-depth description of the procedure adopted. The generation of candidate facial features results in a set composed of two kinds of features: eyes and noses.…”

Section: Candidate Faces Generationmentioning

confidence: 99%

“…The first three modules of the pipeline are the core of our method. The face detector, based on an improved version of a primal approach presented in (Colombo et al, 2006), is able to localize the presence of human-size faces in depth images. The algorithm is based on curvature analysis, ICP-based normalization and Gappy PCA classification.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Occlusions in Face Recognition: a 3D Approach

Colombo¹,

Cusano²,

Schettini³

2009

State of the Art in Face Recognition

Self Cite

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Section: Face Detection and Normalizationmentioning

confidence: 99%

“…The face detector is based on the work presented in (Colombo et al, 2006). The input of the algorithm is supposed to be a single 3D image of the scene.…”

Section: Algorithm Overviewmentioning

confidence: 99%

Section: Candidate Faces Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Occlusions in Face Recognition: a 3D Approach

Colombo¹,

Cusano²,

Schettini³

2009

State of the Art in Face Recognition

Self Cite

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“…It is used to render a high-resolution (HR) face image from multiple low-resolution (LR) [1] and the color face image can be called tensor objects, 3-D (third-order tensor) with column, row, and color modes [2]. In addition, the most active area of biometrics research, namely, that of face recognition, 3-D face detection and recognition using 3-D information with column, row, and depth modes, in other words, a third-order tensor, has emerged as an important research direction [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Face Hallucination with an Error Regression Model and MPCA in RGB Color Space

Asavaskulkiet¹,

Kirawanich²

2013

IJFCC

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Abstract-This paper proposes a novel face super-resolution reconstruction (hallucination) technique, color face images reconstruction of RGB space with an error regression model in multi-linear principal component analysis (MPCA). From hallucination framework, many color face images are explained in RGB space. Then, they can be naturally described as tensors or multi-linear arrays. In this way, the error regression analysis is used to find the error estimation which can be obtained from the existing LR in tensor space. The framework consists of learning and hallucinating process. In learning process is from the mistakes in reconstruct face images of the training dataset by MPCA, then finding the relationship between input and error by regression analysis. In hallucinating process uses normal method by back-projection of MPCA, after that the result is corrected with the error estimation. In this contribution we show that our hallucination technique can be suitable for color face images both in RGB space. By using the MPCA subspace with error regression model, we can generate photorealistic color face images. Our approach is demonstrated by extensive experiments with high-quality hallucinated color faces. In addition, our experiments on face images from FERET database validate our algorithm Index Terms-MPCA, error regression model

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Feature Selection for 2Dand 3DFace Recognition

Bennamoun

Guo

Sohel

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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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 for face recognition. For any 2D/3D face recognition system, feature extraction and selection play a significant role. Currently, both holistic and local features have been intensively investigated in the literature. In this article, fundamental background knowledge of face recognition, including 2D/3D data acquisition, data preprocessing, feature extraction, classification, and performance evaluation, is presented. The state‐of‐the‐art feature extraction algorithms, including 2D holistic feature, 2D local feature, 3D holistic feature, and 3D local feature extraction algorithms, are then described in detail. Finally, feature selection and fusion techniques are presented. The article covers the complete related aspects of feature selection for 2D and 3D face recognition.

show abstract

3D face detection using curvature analysis

Cited by 193 publications

References 7 publications

Occlusions in Face Recognition: a 3D Approach

Occlusions in Face Recognition: a 3D Approach

A Novel Face Hallucination with an Error Regression Model and MPCA in RGB Color Space

Feature Selection for 2Dand 3DFace Recognition

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