Deep learning is one of the most important breakthroughs in the field of artificial intelligence over the last decade. It has achieved great success in speech recognition, natural language processing, computer vision, and multimedia. Many face analysis tasks, including face detection, alignment, reconstruction, and recognition, benefit from the powerful representation learning capability of deep learning techniques. Not only there has been a constantly growing flow of related research papers, but also substantial progress has been achieved in real-world applications such as security, video surveillance, and human-computer interaction.

While substantial progress has been achieved in face analysis with deep learning, many issues still remain and new problems emerge. For instance, the scalability of deep networks to large-scale unconstrained recognition needs be improved. In-the-wild facial attributes recognition with imbalance class distribution is still challenging. The accuracy and efficiency of detecting faces with a wide range of scales in a crowded scene still see a large room for improvement.

This special issue presents a great platform to make a definitive statement about the state of the art by providing a significant collective contribution to this emerging field of study. Specifically, we aim to solicit original contributions that: (1) present state-of-the-art theories related to deep learning for face analysis; (2) develop novel methods and applications; (3) survey the recent progress in this area; and (4) establish benchmark datasets.


The list of possible topics includes, but is not limited to:


  • Deep learning
  • Cross-domain feature learning and fusion
  • Transfer learning
  • Multitask learning
  • Generative adversarial learning
  • Multi-instance learning
  • Weakly supervised learning
  • Reinforcement learning
  • Zero-shot / One-shot learning


  • Face detection
  • Face alignment and tracking
  • Face recognition
  • Face verification
  • Face clustering
  • Face attribute recognition (including age and gender)
  • Facial expression recognition
  • Face hallucination and completion
  • 3D face reconstruction
  • Face parsing
  • Face sketch synthesis and recognition

Paper Submission

Authors are encouraged to submit original work that has not appeared in, nor is in consideration by, other journals. Previously published conference papers can be submitted in extended form (with additional supporting experiments and a more detailed technical description of the method). Manuscripts will be subject to a peer reviewing process and must conform to the authors guidelines available on the IJCV website at "Instructions for Authors" on the right panel.

Manuscripts can be submitted to: by selecting “S.I.: Deep Learning for Face Analysis” in the section “Choose Article Type”.

Important Dates

Submission deadline: 15 Feb 2018 (deadline extended) Submission closed
First review decision: 15 May 2018
Revision deadline: 31 July 2018 31 Aug 2018 (deadline extended)
Final review decision: 15 Oct 2018 We apologize that the decision will only be available in early-mid Nov.
Final manuscript submission: 15 Dec 2018

Guest Editors

Rama Chellappa, University of Maryland, USA
Xiaoming Liu, Michigan State University, USA
Tae-Kyun Kim, Imperial College London, UK
Fernando De la Torre, Facebook, USA
Chen Change Loy, Nanyang Technological University, Singapore