24,293 research outputs found
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
Facial beauty prediction (FBP) is a significant visual recognition problem to
make assessment of facial attractiveness that is consistent to human
perception. To tackle this problem, various data-driven models, especially
state-of-the-art deep learning techniques, were introduced, and benchmark
dataset become one of the essential elements to achieve FBP. Previous works
have formulated the recognition of facial beauty as a specific supervised
learning problem of classification, regression or ranking, which indicates that
FBP is intrinsically a computation problem with multiple paradigms. However,
most of FBP benchmark datasets were built under specific computation
constrains, which limits the performance and flexibility of the computational
model trained on the dataset. In this paper, we argue that FBP is a
multi-paradigm computation problem, and propose a new diverse benchmark
dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty
prediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with
diverse properties (male/female, Asian/Caucasian, ages) and diverse labels
(face landmarks, beauty scores within [1,~5], beauty score distribution), which
allows different computational models with different FBP paradigms, such as
appearance-based/shape-based facial beauty classification/regression model for
male/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP
using different combinations of feature and predictor, and various deep
learning methods. The results indicates the improvement of FBP and the
potential applications based on the SCUT-FBP5500.Comment: 6 pages, 14 figures, conference pape
The analysis of facial beauty: an emerging area of research in pattern analysis
Much research presented recently supports the idea that the human perception of attractiveness is data-driven and largely irrespective of the perceiver. This suggests using pattern analysis techniques for beauty analysis. Several scientific papers on this subject are appearing in image processing, computer vision and pattern analysis contexts, or use techniques of these areas. In this paper, we will survey the recent studies on automatic analysis of facial beauty, and discuss research lines and practical application
The face, beauty, and symmetry: Perceiving asymmetry in beautiful faces
The relationship between bilateral facial symmetry and beauty remains to be clarified. Here, straight head-on photographs of “beautiful” faces from the collections of professional modeling agencies were selected. First, beauty ratings were obtained for these faces. Then, the authors created symmetrical left-left and right-right composites of the beautiful faces and asked a new group of subjects to choose the most attractive pair member. “Same” responses were allowed. No difference between the left-left and right-right composites was revealed but significant differences were obtained between “same” and the left-left or right-right. These results show that subjects detected asymmetry in beauty and suggest that very beautiful faces can be functionally asymmetrical
A New 3D Tool for Planning Plastic Surgery
Face plastic surgery (PS) plays a major role in today medicine. Both for reconstructive and cosmetic surgery, achieving harmony of facial features is an important, if not the major goal. Several systems have been proposed for presenting to patient and surgeon possible outcomes of the surgical procedure. In this paper, we present a new 3D system able to automatically suggest, for selected facial features as nose, chin, etc, shapes that aesthetically match the patient's face. The basic idea is suggesting shape changes aimed to approach similar but more harmonious faces. To this goal, our system compares the 3D scan of the patient with a database of scans of harmonious faces, excluding the feature to be corrected. Then, the corresponding features of the k most similar harmonious faces, as well as their average, are suitably pasted onto the patient's face, producing k+1 aesthetically effective surgery simulations. The system has been fully implemented and tested. To demonstrate the system, a 3D database of harmonious faces has been collected and a number of PS treatments have been simulated. The ratings of the outcomes of the simulations, provided by panels of human judges, show that the system and the underlying idea are effectiv
Some like it hot - visual guidance for preference prediction
For people first impressions of someone are of determining importance. They
are hard to alter through further information. This begs the question if a
computer can reach the same judgement. Earlier research has already pointed out
that age, gender, and average attractiveness can be estimated with reasonable
precision. We improve the state-of-the-art, but also predict - based on
someone's known preferences - how much that particular person is attracted to a
novel face. Our computational pipeline comprises a face detector, convolutional
neural networks for the extraction of deep features, standard support vector
regression for gender, age and facial beauty, and - as the main novelties -
visual regularized collaborative filtering to infer inter-person preferences as
well as a novel regression technique for handling visual queries without rating
history. We validate the method using a very large dataset from a dating site
as well as images from celebrities. Our experiments yield convincing results,
i.e. we predict 76% of the ratings correctly solely based on an image, and
reveal some sociologically relevant conclusions. We also validate our
collaborative filtering solution on the standard MovieLens rating dataset,
augmented with movie posters, to predict an individual's movie rating. We
demonstrate our algorithms on howhot.io which went viral around the Internet
with more than 50 million pictures evaluated in the first month.Comment: accepted for publication at CVPR 201
A New Computer-aided Technique for Planning the Aesthetic Outcome of Plastic Surgery
Plastic surgery plays a major role in today health care. Planning plastic face surgery requires dealing with the elusive concept of attractiveness for evaluating feasible beautification of a particular face. The existing computer tools essentially allow to manually warp 2D images or 3D face scans, in order to produce images simulating possible surgery outcomes. How to manipulate faces, as well as the evaluation of the results, are left to the surgeon's judgement. We propose a new quantitative approach able to automatically suggest effective patient-specific improvements of facial attractiveness. The general idea is to compare the face of the patient with a large database of attractive faces, excluding the facial feature to be improved. Then, the feature of the faces more similar is applied, with a suitable morphing, to the face of the patient. In this paper we present a first application of the general idea in the field of nose surgery. Aesthetically effective rhinoplasty is suggested on the base of the entire face profile, a very important 2D feature for rating face attractivenes
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