Distilling Ordinal Relation and Dark Knowledge for Facial Age Estimation

Supplementary Material for Facial Expression Intensity Estimation Using Ordinal Information

Automatic facial age estimation

V hierarchy. Furthermore, the proposed scheme exploits age based discriminating information taken from two different cues (i.e. facial shape and texture) at the decision level which improves age estimation results. During the process of achieving our main objective of age estimation, this research work also contributes to two associated image processing/analysis areas: i) Face image modeling an...

A new classification method based on pairwise SVM for facial age estimation

This paper presents a practical algorithm for facial age estimation from frontal face image. Facial age estimation generally comprises two key steps including age image representation and age estimation. The anthropometric model used in this study includes computation of eighteen craniofacial ratios and a new accurate skin wrinkles analysis in the first step and a pairwise binary support vector...

Distilling Model Knowledge

Top-performing machine learning systems, such as deep neural networks, large ensembles and complex probabilistic graphical models, can be expensive to store, slow to evaluate and hard to integrate into larger systems. Ideally, we would like to replace such cumbersome models with simpler models that perform equally well. In this thesis, we study knowledge distillation, the idea of extracting the...

Hybrid constraint SVR for facial age estimation

In this paper, facial age estimation is discussed in a novel viewpoint – how to jointly exploit the supervised training data and human annotations to improve the age estimation precision. This is motivated by the lacking of data problem in age estimation and the current web booming. To do so, fuzzy age label is firstly defined, and it is then merged into the Support Vector Regression (SVR) fram...