Face Recognition Using Local Binary Probabilistic Pattern (LBPP) and 2D-DCT Frequency Decomposition

A. Dahmouni, N. Aharrane, K. El Moutaouakil, K. Satori
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引用次数: 10

Abstract

Facial biometrics is an active modality that uses the face characteristics as argument of person identification. In this paper, we propose a new face recognition system basing on the Local Binary Probabilistic Pattern (LBPP) face representation and the global 2D-DCT frequency methods. The Local Binary Probabilistic Pattern is an alternative of the famous LBP descriptor which uses the confidence interval concept to evaluate the current pixel. Then the LBPP transformed images are decomposed in the frequency domain at 2D-DCT method to build a reduce features vector. The suggested approach is tested on ORL and Yale databases. The obtained results are very encouraging: 95.5% for ORL and 100% for Yale databases recognition rate.
基于局部二值概率模式和2D-DCT频率分解的人脸识别
面部生物识别技术是一种利用面部特征作为人的身份识别依据的一种主动识别方式。本文提出了一种基于局部二值概率模式(LBPP)人脸表示和全局2D-DCT频率方法的人脸识别系统。局部二值概率模式是著名的LBP描述符的替代方案,它使用置信区间的概念来评估当前像素。然后对变换后的LBPP图像进行2D-DCT频域分解,构建约简特征向量;该方法在ORL和Yale数据库上进行了测试。所得结果令人鼓舞:ORL识别率为95.5%,耶鲁数据库识别率为100%。
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