1,533 research outputs found
Automated Assessment of Facial Wrinkling: a case study on the effect of smoking
Facial wrinkle is one of the most prominent biological changes that
accompanying the natural aging process. However, there are some external
factors contributing to premature wrinkles development, such as sun exposure
and smoking. Clinical studies have shown that heavy smoking causes premature
wrinkles development. However, there is no computerised system that can
automatically assess the facial wrinkles on the whole face. This study
investigates the effect of smoking on facial wrinkling using a social habit
face dataset and an automated computerised computer vision algorithm. The
wrinkles pattern represented in the intensity of 0-255 was first extracted
using a modified Hybrid Hessian Filter. The face was divided into ten
predefined regions, where the wrinkles in each region was extracted. Then the
statistical analysis was performed to analyse which region is effected mainly
by smoking. The result showed that the density of wrinkles for smokers in two
regions around the mouth was significantly higher than the non-smokers, at
p-value of 0.05. Other regions are inconclusive due to lack of large scale
dataset. Finally, the wrinkle was visually compared between smoker and
non-smoker faces by generating a generic 3D face model.Comment: 6 pages, 8 figures, Accepted in 2017 IEEE SMC International
Conferenc
AGE CLASSIFICATION: BASED ON WRINKLE ANALYSIS
As humans, we are capable to categorize a persons age group from an image of the persons face. This ability has not been pursued in the computer vision community. The method proposed in this article is capable of segregating the given input images into three clusters namely: Baby; Adult; Senior. The computations are based on wrinkle analysis algorithms
Comparative Study of Human Age Estimation with or without Preclassification of Gender and Facial Expression
Age estimation has many useful applications, such as age-based face classification, finding lost children, surveillance monitoring, and face recognition invariant to age progression. Among many factors affecting age estimation accuracy, gender and facial expression can have negative effects. In our research, the effects of gender and facial expression on age estimation using support vector regression (SVR) method are investigated. Our research is novel in the following four ways. First, the accuracies of age estimation using a single-level local binary pattern (LBP) and a multilevel LBP (MLBP) are compared, and MLBP shows better performance as an extractor of texture features globally. Second, we compare the accuracies of age estimation using global features extracted by MLBP, local features extracted by Gabor filtering, and the combination of the two methods. Results show that the third approach is the most accurate. Third, the accuracies of age estimation with and without preclassification of facial expression are compared and analyzed. Fourth, those with and without preclassification of gender are compared and analyzed. The experimental results show the effectiveness of gender preclassification in age estimation
Age Progression/Regression by Conditional Adversarial Autoencoder
"If I provide you a face image of mine (without telling you the actual age
when I took the picture) and a large amount of face images that I crawled
(containing labeled faces of different ages but not necessarily paired), can
you show me what I would look like when I am 80 or what I was like when I was
5?" The answer is probably a "No." Most existing face aging works attempt to
learn the transformation between age groups and thus would require the paired
samples as well as the labeled query image. In this paper, we look at the
problem from a generative modeling perspective such that no paired samples is
required. In addition, given an unlabeled image, the generative model can
directly produce the image with desired age attribute. We propose a conditional
adversarial autoencoder (CAAE) that learns a face manifold, traversing on which
smooth age progression and regression can be realized simultaneously. In CAAE,
the face is first mapped to a latent vector through a convolutional encoder,
and then the vector is projected to the face manifold conditional on age
through a deconvolutional generator. The latent vector preserves personalized
face features (i.e., personality) and the age condition controls progression
vs. regression. Two adversarial networks are imposed on the encoder and
generator, respectively, forcing to generate more photo-realistic faces.
Experimental results demonstrate the appealing performance and flexibility of
the proposed framework by comparing with the state-of-the-art and ground truth.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2017
Automatic age estimation system for face images
Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications. © 2012 Lin et al
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