Multifeature Extreme Ordinal Ranking Machine for Facial Age Estimation

Wei Zhao,Han Wang,Guang-Bin Huang

doi:10.1155/2015/840840

Wei Zhao, Han Wang + Show 1 more

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https://doi.org/10.1155/2015/840840

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Abstract

Recently the state-of-the-art facial age estimation methods are almost originated from solving complicated mathematical optimization problems and thus consume huge quantities of time in the training process. To refrain from such algorithm complexity while maintaining a high estimation accuracy, we propose a multifeature extreme ordinal ranking machine (MFEORM) for facial age estimation. Experimental results clearly demonstrate that the proposed approach can sharply reduce the runtime (even up to nearly one hundred times faster) while achieving comparable or better estimation performances than the state-of-the-art approaches. The inner properties of MFEORM are further explored with more advantages.

Highlights

The trouble is that the age labels have no relationship with each other; that is, each age label is only treated as a separate entity in the training process while, in essence, human’s age labels are Mathematical Problems in Engineering sequential
Craniofacial and skin changes in different age levels would result in unstable random process in feature space, so the kernels used to assess the similarities among different ages could be drifted
The following contributions are made in this paper: (1) Multifeature extreme ordinal ranking machine (MFEORM) for facial age estimation is proposed, which combines the advantages of multifeature space, age’s natural characteristics of ordinal information, and extreme learning machine’s rapid learning rate while achieving similar or even better performances with much less time compared to state-of-the-art methods

Summary

Introduction

With the rapid development of computer vision, pattern recognition, and biometrics, more and more attention has been paid to computer-based human facial age estimation, which will be utilized in the scenarios where an individual’s age needs to be obtained without identifying other irrelevant personal information, such as electronic customer information management [1, 2], human-computer interaction (HCI) [3], security surveillance monitoring [4, 5], age-based visual advertisement, and even entertainment. Calculation complexity would be a heavy burden for improving efficiency Recognizing this point, we propose a multifeature extreme ordinal ranking machine (MFEORM) for facial age estimation. (1) Multifeature extreme ordinal ranking machine (MFEORM) for facial age estimation is proposed, which combines the advantages of multifeature space, age’s natural characteristics of ordinal information, and extreme learning machine’s rapid learning rate while achieving similar or even better performances with much less time compared to state-of-the-art methods. Huang et al [26] have proved that, for the purpose of letting SLFNs serve as universal approximators, we can randomly choose the hidden layer parameters and analytically determine the output weight vectors connecting the output layer and hidden layer In this case, for additive nodes, activation functions can be arbitrary bounded nonconstant continuous piecewise functions g: R → R; for RBF.

Proposed Method

Experiments

Conclusion