BIROn - Birkbeck Institutional Research Online

    D2C: deep cumulatively and comparatively learning for human age estimation

    Li, K. and Xing, J. and Hu, W. and Maybank, Stephen (2017) D2C: deep cumulatively and comparatively learning for human age estimation. Pattern Recognition 66 , pp. 95-105. ISSN 0031-3203.

    [img]
    Preview
    Text
    18811.pdf - Author's Accepted Manuscript

    Download (1MB) | Preview

    Abstract

    Age estimation from face images is an important yet difficult task in computer vision. Its main difficulty lies in how to design aging features that remain discriminative in spite of large facial appearance variations. Meanwhile, due to the difficulty of collecting and labeling datasets that contain sufficient samples for all possible ages, the age distributions of most benchmark datasets are often imbalanced, which makes this problem more challenge. In this work, we try to solve these difficulties by means of the mainstream deep learning techniques. Specifically, we use a convolutional neural network which can learn discriminative aging features from raw face images without any handcrafting. To combat the sample imbalance problem, we propose a novel cumulative hidden layer which is supervised by a point-wise cumulative signal. With this cumulative hidden layer, our model is learnt indirectly using faces with neighbouring ages and thus alleviate the sample imbalance problem. In order to learn more effective aging features, we further propose a comparative ranking layer which is supervised by a pair-wise comparative signal. This comparative ranking layer facilitates aging feature learning and improves the performance of the main age estimation task. In addition, since one face can be included in many different training pairs, we can make full use of the limited training data. It is noted that both of these two novel layers are differentiable, so our model is end-to-end trainable. Extensive experiments on the two of the largest benchmark datasets show that our deep age estimation model gains notable advantage on accuracy when compared against existing methods.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Age estimation, Deep learning, Convolutional neural network
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 31 May 2017 14:50
    Last Modified: 07 Jan 2018 01:10
    URI: http://eprints.bbk.ac.uk/id/eprint/18811

    Statistics

    Downloads
    Activity Overview
    34Downloads
    128Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item