Although deep learning techniques have largely improved face recognition, unconstrained surveillance face recognition (FR) is still an unsolved challenge, due to the limited training data and the gap of domain distribution.
To address the conflicts among multiple tasks and meet the different demands of tasks, a Multi-Level Channel Attention (MLCA) module is integrated into each task-specific analysis subnet, which can adaptively select the features from optimal levels and channels to perform the desired tasks.
Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions.
Ranked #2 on Face Verification on CALFW
Face photo-sketch synthesis and recognition has many applications in digital entertainment and law enforcement.
The training of a deep face recognition system usually faces the interference of label noise in the training data.
Then, rethinking person ReID as a zero-shot learning problem, we propose the Mixed High-Order Attention Network (MHN) to further enhance the discrimination and richness of attention knowledge in an explicit manner.
Ranked #4 on Person Re-Identification on CUHK03-C
Racial bias is an important issue in biometric, but has not been thoroughly studied in deep face recognition.
Labeled Faces in the Wild (LFW) database has been widely utilized as the benchmark of unconstrained face verification and due to big data driven machine learning methods, the performance on the database approaches nearly 100%.
We extend the classical linear discriminant analysis (LDA) technique to linear ranking analysis (LRA), by considering the ranking order of classes centroids on the projected subspace.
The success of sparse representation based classification (SRC) has largely boosted the research of sparsity based face recognition in recent years.