More Like this

1. Identical Twins as a Facial Similarity Benchmark for Human Facial Recognition

   https://doi.org/10.1109/BIOSIG52210.2021.9548299  

   McCauley, John; Soleymani, Sobhan; Williams, Brady; Dando, John; Nasrabadi, Nasser; Dawson, Jeremy (September 2021, International Conference of the Biometrics Special Interest Group (BIOSIG))
2. FACIAL GRID TRANSFORMATION: A NOVEL FACE REGISTRATION APPROACH FOR IMPROVING FACIAL ACTION UNIT RECOGNITION

   https://doi.org/10.1109/ICIP.2014.7025283  

   Han, Shizhong; Meng, Zibo; Liu, Ping; Tong, Yan (October 2014, Proceedings - International Conference on Image Processing)

   Face registration is a major and critical step for face analysis. Existing facial activity recognition systems often employ coarse face alignment based on a few fiducial points such as eyes and extract features from equal-sized grid. Such extracted features are susceptible to variations in face pose, facial deformation, and person-specific geometry. In this work, we propose a novel face registration method named facial grid transformation to improve feature extraction for recognizing facial Action Units (AUs). Based on the transformed grid, novel grid edge features are developed to capture local facial motions related to AUs. Extensive experiments on two wellknown AU-coded databases have demonstrated that the proposed method yields significant improvements over the methods based on equal-sized grid on both posed and more importantly, spontaneous facial displays. Furthermore, the proposed method also outperforms the state-of-the-art methods using either coarse alignment or mesh-based face registration. 

   more » « less
3. Macromolecular-clustered facial amphiphilic antimicrobials

   https://doi.org/10.1038/s41467-018-07651-7  

   Rahman, Md Anisur; Bam, Marpe; Luat, Edgar; Jui, Moumita Sharmin; Ganewatta, Mitra S.; Shokfai, Tinom; Nagarkatti, Mitzi; Decho, Alan W.; Tang, Chuanbing (December 2018, Nature Communications)

   null (Ed.)
4. MyTimeMachine: Personalized Facial Age Transformation

   https://doi.org/10.1145/3731172  

   Qi, Luchao; Wu, Jiaye; Gong, Bang; Wang, Annie N; Jacobs, David W; Sengupta, Roni (August 2025, ACM Transactions on Graphics)

   Facial aging is a complex process, highly dependent on multiple factors like gender, ethnicity, lifestyle, etc., making it extremely challenging to learn a global aging prior to predict aging for any individual accurately. Existing techniques often produce realistic and plausible aging results, but the re-aged images often do not resemble the person's appearance at the target age and thus need personalization. In many practical applications of virtual aging, e.g. VFX in movies and TV shows, access to a personal photo collection of the user depicting aging in a small time interval (20~40 years) is often available. However, naive attempts to personalize global aging techniques on personal photo collections often fail. Thus, we propose MyTimeMachine (MyTM), a method that combines a global aging prior with a personalized photo collection (ranging from as few as 10 images, ideally 50) to learn individualized age transformations. We introduce a novel Adapter Network that combines personalized aging features with global aging features and generates a re-aged image with StyleGAN2. We also introduce three loss functions to personalize the Adapter Network with personalized aging loss, extrapolation regularization, and adaptive w-norm regularization. Our method demonstrates strong performance on fair-use imagery of widely recognizable individuals, producing photorealistic and identity-consistent age transformations that generalize well across diverse appearances. It also extends naturally to video, delivering high-quality, temporally consistent results that closely resemble actual appearances at target ages—outperforming state-of-the-art approaches. 

   more » « less
5. Attributes in Multiple Facial Images

   https://doi.org/10.1109/FG.2018.00053  

   Liu, Xudong; Guo, Guodong (May 2018, IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018))

   Facial attribute recognition is conventionally computed from a single image. In practice, each subject may have multiple face images. Taking the eye size as an example, it should not change, but it may have different estimation in multiple images, which would make a negative impact on face recognition. Thus, how to compute these attributes corresponding to each subject rather than each single image is a profound work. To address this question, we deploy deep training for facial attributes prediction, and we explore the inconsistency issue among the attributes computed from each single image. Then, we develop two approaches to address the inconsistency issue. Experimental results show that the proposed methods can handle facial attribute estimation on either multiple still images or video frames, and can correct the incorrectly annotated labels. The experiments are conducted on two large public databases with annotations of facial attributes. 

   more » « less