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Feature Vector Compression Based on Least Error Quantization
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
We propose a distinctive feature vector compression method based on least error quantization. This method can be applied to several biometrics methods using feature vectors, and allows us to significantly reduce the memory size of feature vectors without degrading the recognition performance. In this paper, we prove that minimizing quantization error between the compressed and original vectors is most effective to control the performance in face recognition. A conventional method uses
doi:10.1109/cvprw.2016.18
dblp:conf/cvpr/KawaharaY16
fatcat:5ox2aeusp5gyrfduhlyk2kvzxq