Large-scale Supervised Hierarchical Feature Learning for Face Recognition
This work addresses face recognition for applications like security and embedded systems, but it is incremental as it builds on existing hierarchical and supervised learning techniques.
The paper tackles face recognition by developing a supervised hierarchical feature learning algorithm that searches features at three granularity levels and trains a linear classifier using ADMM on large-scale data, achieving notable accuracy improvements on FRGC and LFW datasets compared to existing methods.
This paper proposes a novel face recognition algorithm based on large-scale supervised hierarchical feature learning. The approach consists of two parts: hierarchical feature learning and large-scale model learning. The hierarchical feature learning searches feature in three levels of granularity in a supervised way. First, face images are modeled by receptive field theory, and the representation is an image with many channels of Gaussian receptive maps. We activate a few most distinguish channels by supervised learning. Second, the face image is further represented by patches of picked channels, and we search from the over-complete patch pool to activate only those most discriminant patches. Third, the feature descriptor of each patch is further projected to lower dimension subspace with discriminant subspace analysis. Learned feature of activated patches are concatenated to get a full face representation.A linear classifier is learned to separate face pairs from same subjects and different subjects. As the number of face pairs are extremely large, we introduce ADMM (alternative direction method of multipliers) to train the linear classifier on a computing cluster. Experiments show that more training samples will bring notable accuracy improvement. We conduct experiments on FRGC and LFW. Results show that the proposed approach outperforms existing algorithms under the same protocol notably. Besides, the proposed approach is small in memory footprint, and low in computing cost, which makes it suitable for embedded applications.