Abstract
Face clustering is an important topic in computer vision. It aims to put together facial images that belong to the same person. Spectral clustering-based algorithms are often used for accurate face clustering. However, a big occlusion matrix is usually needed to deal with the noise and sparse outlying terms, which makes the sparse coding process computationally expensive. Thus spectral clustering-based algorithms are difficult to extend to large scale datasets. In this paper, we use the image gradient feature descriptor and scalable Sparse Subspace Clustering algorithm for large scale and high accuracy face clustering. Within the image gradient feature descriptor, the scalable Sparse Subspace Clustering algorithm can be used in large scale face datasets without sacrificing clustering performance. Experimental results show that our algorithm is robust to illumination, occlusion, and achieves a relatively high clustering accuracy on the Extended Yale B and AR datasets.
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Liu, M., Li, Q., Sun, Z., Deng, Q. (2018). Face Clustering Utilizing Scalable Sparse Subspace Clustering and the Image Gradient Feature Descriptor. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_34
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