Abstract
Face recognition has attracted a lot of attention in the last decades and achieved high recognition rate under controlled environment. More and more researchers now focus on face recognition in the wild, which is difficult because of the variance of pose, illumination, occlusion and so on. In this paper, we aim to solve this problem by combining image retrieval and feature weighting. By image retrieval method, we can find those face images in the gallery set which are the most similar to the probe face image. After getting similar face subset, feature weighting is then executed on this subset. This process includes two steps. In the first step, we learn a weight for each single feature in this subset by finding its nearest neighbor. In the second step, inspired by frequent item mining method we learn a weight for a group of features. In the testing process, by weighted nearest neighbor voting for both single and grouped features, we classify the probe image to the class which has the highest similarity score. We evaluate our method on AR and Pubfig83 face data sets. Experiment shows that our method has achieved state-of-the-art performance.
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Wang, Y., Cheng, H., Zheng, Y., Yang, L. (2014). Face Recognition in the Wild by Mining Frequent Feature Itemset. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_35
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DOI: https://doi.org/10.1007/978-3-662-45643-9_35
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