Semantic facial scores and compact deep transferred descriptors for scalable face image retrieval

Rasoul Banaeeyan*, Haris Lye, Mohammad Faizal Ahmad Fauzi, Hezerul Abdul Karim, John See

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Face retrieval systems intend to locate index of identical faces to a given query face. The performance of these systems heavily relies on the careful analysis of different facial attributes (gender, race, etc.) since these attributes can tolerate some degrees of geometrical distortion, expressions, and occlusions. However, solely employing facial scores fail to add scalability. Besides, owing to the discriminative power of CNN (convolutional neural network) features, recent works have employed a complete set of deep transferred CNN features with a large dimensionality to obtain enhancement; yet these systems require high computational power and are very resource-demanding. This study aims to exploit the distinctive capability of semantic facial attributes while their retrieval results are refined by a proposed subset feature selection to reduce the dimensionality of the deep transferred descriptors. The constructed compact deeply transferred descriptors (CDTD) not only have a largely reduced dimension but also more discriminative power. Lastly, we have proposed a new performance metric which is tailored for the case of face retrieval called ImAP (Individual Mean Average Precision) and is used to evaluate retrieval results. Multiple experiments on two scalable face datasets demonstrated the superior performance of the proposed CDTD model outperforming state-of-the-art face retrieval results.

Original languageEnglish
Pages (from-to)111-128
Number of pages18
Early online date7 May 2018
Publication statusPublished - 25 Sept 2018


  • Compact deep transferred descriptor
  • Deep transferred CNN feature
  • Facial attribute classification
  • Scalable face retrieval

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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