One key challenge of face detection is the large appearance variations due to some real-world factors, such as viewpoint, extreme illuminations and expression changes, which lead to the large intra-class variations and making the detection algorithm is not robust enough. In this paper, we propose a locality-sensitive support vector machine using kernel combination (LS-KC-SVM) algorithm to solve the above two problems. First, we employ the locality-sensitive SVM (LSSVM) to construct a local model on each local region, which can handle the classification task easier due to smaller within-class variation. Second, motivated by the idea that local features are more robust compared with global features, we use multiple local CNNs to jointly learn local facial features because of the powerful strength of CNN learning characteristic. In order to use this property of local features effectively, we apply the global and local kernels to the features and introduce the combination kernel to the LSSVM. Extensive experiments demonstrate the robustness and efficiency of our algorithm by comparing it with several popular face detection algorithms on the widely used CMU+MIT dataset and FDDB dataset.
['Qin-Qin Tao', 'Shu Zhan', 'Xiao-Hong Li', 'Toru Kurihara']
Robust face detection using local CNN and SVM based on kernel combination