Efficient Face Alignment via Locality-constrained Representation for Robust Recognition
This addresses a practical challenge in face recognition for real-time applications, but it is incremental as it builds on existing locality-constrained methods.
The paper tackles the problem of face recognition performance dropping due to severe misalignment by proposing a misalignment-robust locality-constrained representation algorithm, which significantly outperforms state-of-the-art methods in efficiency and scalability with better performance on public datasets.
Practical face recognition has been studied in the past decades, but still remains an open challenge. Current prevailing approaches have already achieved substantial breakthroughs in recognition accuracy. However, their performance usually drops dramatically if face samples are severely misaligned. To address this problem, we propose a highly efficient misalignment-robust locality-constrained representation (MRLR) algorithm for practical real-time face recognition. Specifically, the locality constraint that activates the most correlated atoms and suppresses the uncorrelated ones, is applied to construct the dictionary for face alignment. Then we simultaneously align the warped face and update the locality-constrained dictionary, eventually obtaining the final alignment. Moreover, we make use of the block structure to accelerate the derived analytical solution. Experimental results on public data sets show that MRLR significantly outperforms several state-of-the-art approaches in terms of efficiency and scalability with even better performance.