Human identification based on facial traits has made remarkable progress thanks to deep learning-based methods, but there are still challenges, for example related to the effects of occlusions, retrieval from large databases, and multi-modal representations. The recent pandemic has reminded how face recognition systems are deeply affected by facial occlusions due to large sections of the population wearing masks (Randhawa et al., 2022). Retrieving face images from large datasets instead requires learning representations that are compact and balanced to maximize performance (Pidhorskyi et al., 2018)]. The use of multiple modalities, like appearance and depth, instead requires reaching a balance between acquisition cost and capture time even in uncontrolled conditions (Piccirilli et al., 2016).

Face Modeling and Tracking

2 minute read

Active Appearance Models (AAMs) represent facial images with generative models for both shape and appearance of the face. Despite their success, they enjoy l...


  1. ISVC
    Learning Representations for Masked Facial Recovery Randhawa, Z. A., Patel, S., Adjeroh, D. A., and Doretto, G. In Advances in Visual Computing, 2022. Oral abstract bibTeX arXiv pdf doi
  2. ACCV
    Deep supervised hashing with spherical embedding Pidhorskyi, S., Jones, Q., Motiian, S., Adjeroh, D., and Doretto, G. In Proceedings of The Asian Conference on Computer Vision, 2018. abstract bibTeX pdf
    A Mobile Structured Light System for 3D Face Acquisition Piccirilli, M., Doretto, G., Ross, A., and Adjeroh, D. IEEE Sensors Journal, 2016. abstract bibTeX pdf