Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks

Jun Cheng Chen, Rajeev Ranjan, Swami Sankaranarayanan, Amit Kumar, Ching Hui Chen, Vishal M. Patel, Carlos D. Castillo, Rama Chellappa

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Over the last 5 years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. This has been made possible due to the availability of large annotated datasets, a better understanding of the non-linear mapping between input images and class labels as well as the affordability of GPUs. In this paper, we present the design details of a deep learning system for unconstrained face recognition, including modules for face detection, association, alignment and face verification. The quantitative performance evaluation is conducted using the IARPA Janus Benchmark A (IJB-A), the JANUS Challenge Set 2 (JANUS CS2), and the Labeled Faces in the Wild (LFW) dataset. The IJB-A dataset includes real-world unconstrained faces of 500 subjects with significant pose and illumination variations which are much harder than the LFW and Youtube Face datasets. JANUS CS2 is the extended version of IJB-A which contains not only all the images/frames of IJB-A but also includes the original videos. Some open issues regarding DCNNs for face verification problems are then discussed.

Original languageEnglish (US)
Pages (from-to)272-291
Number of pages20
JournalInternational Journal of Computer Vision
Issue number2-4
StatePublished - Apr 1 2018

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


  • Deep learning
  • Face detection/association
  • Face verification
  • Fiducial detection
  • Metric learning

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