TY - GEN
T1 - Statik ve dinamik özniteliklere dayali yüz güzelliǧ ianaliż
AU - Kalayci, Sacide
AU - Ekenel, Hazim Kemal
AU - Gunes, Hatice
PY - 2014
Y1 - 2014
N2 - Analysing and measuring beauty and attractiveness has become a passion since the beginning of the human existence. Providing solutions to this mystery has been the pursuit of philosophers, artists, and anthropologists for centuries. More recently, the computer science community has attempted to propose computational models for the perception and representation of beauty by cross-fertilizing technological advancements in various fields including signal processing, computer vision and machine learning. Most of the proposed studies attempt to describe facial attractiveness via a structural model of the face obtained from a static facial image. While a static image provides limited information about facial attractiveness, using a video clip that contains information about motion, gestures, and facial expressions provides a richer and more dynamic way of analysing beauty. In this work, along with static features obtained from images, dynamic features obtained from video clips are also used to evaluate facial attractiveness. Support vector machine (SVM) and random forest (RF) are utilised to create and train models of attractiveness and evaluate the features extracted. Experimental results show that combining static and dynamic features improve performance over using either of these features alone, and SVM provides the best recognition performance.
AB - Analysing and measuring beauty and attractiveness has become a passion since the beginning of the human existence. Providing solutions to this mystery has been the pursuit of philosophers, artists, and anthropologists for centuries. More recently, the computer science community has attempted to propose computational models for the perception and representation of beauty by cross-fertilizing technological advancements in various fields including signal processing, computer vision and machine learning. Most of the proposed studies attempt to describe facial attractiveness via a structural model of the face obtained from a static facial image. While a static image provides limited information about facial attractiveness, using a video clip that contains information about motion, gestures, and facial expressions provides a richer and more dynamic way of analysing beauty. In this work, along with static features obtained from images, dynamic features obtained from video clips are also used to evaluate facial attractiveness. Support vector machine (SVM) and random forest (RF) are utilised to create and train models of attractiveness and evaluate the features extracted. Experimental results show that combining static and dynamic features improve performance over using either of these features alone, and SVM provides the best recognition performance.
KW - Facial attractivenes
KW - random forest
KW - static and dynamic features
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84903765279&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84903765279&partnerID=8YFLogxK
U2 - 10.1109/SIU.2014.6830581
DO - 10.1109/SIU.2014.6830581
M3 - Conference contribution
AN - SCOPUS:84903765279
SN - 9781479948741
T3 - 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings
SP - 1722
EP - 1725
BT - 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings
PB - IEEE Computer Society
T2 - 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014
Y2 - 23 April 2014 through 25 April 2014
ER -