timofter

4 ms·

1.
by timofter 11y ago
Please check our research papers linked on the webpage. Our data consists only from normal (or natural looking) face images in the wild (from IMDB, Wiki, and/or BLINQ user profiles). On such data we get very good apparent age predictio
2.
by timofter 11y ago
The ratings are "like" (positive) and "dislike" (negative). If men have ratings close to uniform distribution (44.81% are "like" ratings, 55.19% are "dislike"), women with only 8.58% "like"
3.
by timofter 11y ago
Hi sethbannon, I am Radu, one of the authors. We thought at similar experiments, however psychology is not our expertise. If you check our paper on hotness/attractiveness ( http://www.vision.ee.ethz.ch/~timofter/pub
4.
by timofter 11y ago
Hi tsumnia, I am Radu, one of the authors. After 2 years we face almost the same issues, but probably we cope differently with them. Note that our solutions are fully described in the two papers mentioned on our faces.ethz.ch page. For attr
5.
by timofter 11y ago
The first processing step consist from (human) face detection. We use the standard OpenCV for our faces.ethz.ch demonstrator. A failure of this step is likely to propagate in the unreliable/wrong attractiveness prediction. For attracti
6.
by timofter 11y ago
Yes, it is a foreseen application.
7.
by timofter 11y ago
Hi fatjokes, I am Radu, one of the authors. As mentioned in our article ( http://arxiv.org/abs/1510.07867v1 ), the training dataset for attractiveness is from Blinq. The underage and above 37 years old face images were d
8.
by timofter 11y ago
Yes, this was the data we worked with for attractiveness modeling. The algorithm learns from millions of ratings from males to females and from females to males, all heterosexuals. No underage or above 37 years old, and mostly people from Z