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Face age classification based on a deep hybrid model

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Abstract

Face age estimation, a computer vision task facing numerous challenges due to its potential applications in identity authentication, human–computer interface, video retrieval and robot vision, has been attracting increasing attention. In recent years, the deep convolutional neural networks (DCNN) have achieved state-of-the-art performance in age classification of face images. We propose a deep hybrid framework for age classification by exploiting DCNN as the raw feature extractor along with several effective methods, including fine-tuning the DCNN into a fine-tuned deep age feature extraction (FDAFE) model, introducing a new method of feature extracting, applying the maximum joint probability classifier to age classification and a strategy to incorporate information from face images more effectively to improve estimation capabilities further. In addition, we pre-process the original image to represent age information more accurately. Based on the discriminative and compact framework, state-of-the-art performance on several face image data sets has been achieved in terms of classification accuracy.

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Authors and Affiliations

  1. Electronic Information School, Wuhan University, Wuhan, China

    Liming Chen, Cien Fan, Haiyan Yang, Shiyong Hu, Lian Zou & Dexiang Deng

  2. School of Physics and Electric Engineering, Leshan Normal University, Leshan, China

    Liming Chen

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  1. Liming Chen
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Correspondence to Dexiang Deng.

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Chen, L., Fan, C., Yang, H. et al. Face age classification based on a deep hybrid model. SIViP 12, 1531–1539 (2018). https://doi.org/10.1007/s11760-018-1309-6

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