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Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval

  • Bor-Chun Chen
  • Chu-Song Chen
  • Winston H. Hsu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)

Abstract

Recently, promising results have been shown on face recognition researches. However, face recognition and retrieval across age is still challenging. Unlike prior methods using complex models with strong parametric assumptions to model the aging process, we use a data-driven method to address this problem. We propose a novel coding framework called Cross-Age Reference Coding (CARC). By leveraging a large-scale image dataset freely available on the Internet as a reference set, CARC is able to encode the low-level feature of a face image with an age-invariant reference space. In the testing phase, the proposed method only requires a linear projection to encode the feature and therefore it is highly scalable. To thoroughly evaluate our work, we introduce a new large-scale dataset for face recognition and retrieval across age called Cross-Age Celebrity Dataset (CACD). The dataset contains more than 160,000 images of 2,000 celebrities with age ranging from 16 to 62. To the best of our knowledge, it is by far the largest publicly available cross-age face dataset. Experimental results show that the proposed method can achieve state-of-the-art performance on both our dataset as well as the other widely used dataset for face recognition across age, MORPH dataset.

Keywords

Face Recognition Aging 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bor-Chun Chen
    • 1
  • Chu-Song Chen
    • 1
  • Winston H. Hsu
    • 2
  1. 1.Institute of Information ScienceAcademia SinicaTaipeiTaiwan
  2. 2.National Taiwan UniversityTaipeiTaiwan

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