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 non-uniform quantizer which minimizes the quantization error in terms of L2-distance. However, face recognition methods often use metrics other than L2-distance. Our method can calculate the quantized vectors in arbitrary metrics such as Lp-distance (0
['Tomokazu Kawahara', 'Osamu Yamaguchi']
Feature Vector Compression Based on Least Error Quantization