Local feature descriptor has been widely used in computer vision field due to their excellent discriminative power and strong robustness. However, the forms of such local descriptors are predefined in the hand-crafted way, which requires strong domain knowledge to design them. In this paper, we propose a simple and efficient Spherical Hashing based Binary Codes (SHBC) feature learning method to learn a discriminative and robust binary face descriptor in the data-driven way. Firstly, we extract patch-wise pixel difference vectors (PDVs) by computing the difference between center patch and its neighboring patches. Then, inspired by the fact that hypersphere provide much stronger power in defining a tighter closed region in the original data space than hyperplane, we learn a hypersphere-based hashing function to map these PDVs into low-dimensional binary codes by an efficient iterative optimization process, which achieves both balanced bits partitioning of data points and independence between hashing functions. In order to better capture the semantic information of the dataset, our SHBC also can be used with supervised data embedding method, such as Canonical Correlation Analysis (CCA), namely Supervised-SHBC (S-SHBC). Lastly, we cluster and pool these learned binary codes into a histogram-based feature that describes the co-occurrence of binary codes. And we consider the histogram-based feature as our final feature representation for each face image. We investigate the performance of our SHBC and S-SHBC on FERET, CAS-PEAL-R1, LFW and PaSC databases. Extensive experimental results demonstrate that our SHBC descriptor outperforms other state-of-the-art face descriptors.
['Lei Tian', 'Chunxiao Fan', 'Yue Ming']
Learning spherical hashing based binary codes for face recognition