On the Quantitative Estimation of Short-Term Aging in Human Faces

2013 21st Signal Processing and Communications Applications Conference (SIU)

Gevers

2013

Özetçe -Yüz imgelerinden yaş kestirimi genellikle görüntü tabanlı sistemler ile gerçekleştirilir. Bu çalışmada yaş kestirimi problemi için yakın zamanda önerilen bir dizi zaman-uzamsal öznitelikler degerlendirilmekte ve video görüntülerinden yaş kestirimi yapılmaktadır. Yüzden çıkartılan nirengi noktalarının gülüş sırasında oluşan hareket dinamiklerinin bu problemin çözümünde faydalı oldugu ve sadece imge tabanlı öznitelikleri kullanan sistemleri iyileştirdigi gösterilmektedir. Anahtar Kelimeler-Yaş kestirimi, yüz ifadesi analizi.Abstract-Age estimation from face images traditionally focuses on appearance features. In this paper, we evaluate a number of recently proposed spatio-temporal features for the problem of age estimation from face videos. It is shown that temporal features based on movement dynamics of facial landmarks during a smile expression can improve age estimation over using only appearance-based features.

Section: Iii̇lgi̇li̇ çAlişmalarunclassified

Age estimation using facial expression dynamics

2013 21st Signal Processing and Communications Applications Conference (SIU)

Gevers

2013

“…Tiddeman et al [28] also developed prototype models for aging face images using texture information. In [18], a quantitative approach to face evolution due to aging is presented.…”

Section: Related Workmentioning

confidence: 99%

A smile can reveal your age

Gevers

Proceedings of the 20th ACM International Conference on Multimedia

et al. 2012

Estimation of a person's age from the facial image has many applications, ranging from biometrics and access control to cosmetics and entertainment. Many image-based methods have been proposed for this problem. In this paper, we propose a method for the use of dynamic features in age estimation, and show that 1) the temporal dynamics of facial features can be used to improve imagebased age estimation; 2) considered alone, static image-based features are more accurate than dynamic features. We have collected and annotated an extensive database of face videos from 400 subjects with an age range between 8 and 76, which allows us to extensively analyze the relevant aspects of the problem. The proposed system, which fuses facial appearance and expression dynamics, performs with a mean absolute error of 4.81 (±4.87) years. This represents a significant improvement of accuracy in comparison to the sole use of appearance-based features.

Combining Facial Dynamics With Appearance for Age Estimation

Alnajar

IEEE Trans. on Image Process.

et al. 2015

100

Estimating the age of a human from the captured images of his/her face is a challenging problem. In general, the existing approaches to this problem use appearance features only. In this paper, we show that in addition to appearance information, facial dynamics can be leveraged in age estimation. We propose a method to extract and use dynamic features for age estimation, using a person's smile. Our approach is tested on a large, gender-balanced database with 400 subjects, with an age range between 8 and 76. In addition, we introduce a new database on posed disgust expressions with 324 subjects in the same age range, and evaluate the reliability of the proposed approach when used with another expression. State-of-the-art appearance-based age estimation methods from the literature are implemented as baseline. We demonstrate that for each of these methods, the addition of the proposed dynamic features results in statistically significant improvement. We further propose a novel hierarchical age estimation architecture based on adaptive age grouping. We test our approach extensively, including an exploration of spontaneous versus posed smile dynamics, and gender-specific age estimation. We show that using spontaneity information reduces the mean absolute error by up to 21%, advancing the state of the art for facial age estimation.