Bayesian tangent shape model: estimating shape and pose parameters via Bayesian inference

“…ASM utilized the local texture information in search of a better template, and AAM constructed appearance models according to shape parameters and global texture constraints. After ASM and AAM, Zhou et al [3] introduced Bayesian Tangent Shape Model (BTSM) with an EM-based method to implement the MAP estimation, Liang et al [4] utilized a Constrained Markov Network for accurate face alignment, and Boosting Appearance Models (BAM) [5] presented a discriminative method with boosting algorithm and rectangular Haar-like features, which resulted in outstanding accuracy and robustness. Enlightened by BAM, the speed of face alignment can be improved by more discriminative features and boosting classifiers bring in the benefit of computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Face Alignment Using Boosting and Evolutionary Search

Zhang

Liu

Poel

et al. 2010

Computer Vision – ACCV 2009

View full text Add to dashboard Cite

Abstract. In this paper, we present a face alignment approach using granular features, boosting, and an evolutionary search algorithm. Active Appearance Models (AAM) integrate a shape-texture-combined morphable face model into an efficient fitting strategy, then Boosting Appearance Models (BAM) consider the face alignment problem as a process of maximizing the response from a boosting classifier. Enlightened by AAM and BAM, we present a framework which implements improved boosting classifiers based on more discriminative features and exhaustive search strategies. In this paper, we utilize granular features to replace the conventional rectangular Haar-like features, to improve discriminability, computational efficiency, and a larger search space. At the same time, we adopt the evolutionary search process to solve the deficiency of searching in the large feature space. Finally, we test our approach on a series of challenging data sets, to show the accuracy and efficiency on versatile face images.

show abstract

“…Active Shape Models (ASMs) [5] have undergone several changes since the classical ASM was proposed in [6] and have recently become more robust [14,20,32] and hence more popular for accomplishing this task as they are not only faster, but also more accurate when dealing with unseen variations that are not present in training images.…”

Section: Introductionmentioning

confidence: 99%

Contourlet appearance model for facial age estimation

Luu

Seshadri

Savvides

et al. 2011

2011 International Joint Conference on Biometrics (IJCB)

View full text Add to dashboard Cite

In this paper we propose a novel Contourlet Appearance Model (CAM) that is more accurate and faster at localizing facial landmarks than Active Appearance Models (AAMs). Our CAM also has the ability to not only extract holis tic texture information, as AAMs do, but can also extract local texture information using the Nonsubsampled Con tourlet Transform (NSCT). We demonstrate the efficiency of our method by applying it to the problem of facial age es timation. Compared to previously published age estimation techniques, our approach yields more accurate results when tested on various face aging databases.

show abstract

Bayesian tangent shape model: estimating shape and pose parameters via Bayesian inference

Abstract: 12In this paper we study the

Cited by 81 publications

References 15 publications

Audio Visual Speech Recognition and Segmentation Based on DBN Models

Audio Visual Speech Recognition and Segmentation Based on DBN Models

Face Alignment Using Boosting and Evolutionary Search

Contourlet appearance model for facial age estimation

Contact Info

Product

Resources

About