Learning to name faces: a multimodal learning scheme for search-based face annotation

  • Authors:

  • Dayong Wang;Steven C.H. Hoi;Pengcheng Wu;Jianke Zhu;Ying He;Chunyan Miao

  • Affiliations:

  • Nanyang Technological University, Singapore, Singapore;Nanyang Technological University, Singapore, Singapore;Nanyang Technological University, Singapore, Singapore;Zhejiang University, Hangzhou, China;Nanyang Technological University, Singapore, Singapore;Nanyang Technological University, Singapore, Singapore

  • Venue:
  • Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2013

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Abstract

Automated face annotation aims to automatically detect human faces from a photo and further name the faces with the corresponding human names. In this paper, we tackle this open problem by investigating a search-based face annotation (SBFA) paradigm for mining large amounts of web facial images freely available on the WWW. Given a query facial image for annotation, the idea of SBFA is to first search for top-n similar facial images from a web facial image database and then exploit these top-ranked similar facial images and their weak labels for naming the query facial image. To fully mine those information, this paper proposes a novel framework of Learning to Name Faces (L2NF) -- a unified multimodal learning approach for search-based face annotation, which consists of the following major components: (i) we enhance the weak labels of top-ranked similar images by exploiting the "label smoothness" assumption; (ii) we construct the multimodal representations of a facial image by extracting different types of features; (iii) we optimize the distance measure for each type of features using distance metric learning techniques; and finally (iv) we learn the optimal combination of multiple modalities for annotation through a learning to rank scheme. We conduct a set of extensive empirical studies on two real-world facial image databases, in which encouraging results show that the proposed algorithms significantly boost the naming accuracy of search-based face annotation task.