基于小波变换和支持向量机的压缩人脸识别

M. SujathaB, C. T. Madiwalar, K. Sureshbabu, B. RajaK, R. VenugopalK.
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引用次数: 9

摘要

生物识别技术被用来有效地识别一个人,并应用于几乎所有的日常活动中。本文提出了一种基于压缩的人脸识别方法,采用离散小波变换(DWT)和支持向量机(SVM)。为了减少执行时间和内存,引入了利用平均技术将单个人的多幅图像转换为一幅图像的新概念。对平均后的人脸图像进行小波变换,得到近似带和详细带。将LL波段系数作为支持向量机的输入,得到支持向量。基于算术加法,将DWT和SV的LL系数进行融合,提取最终特征。利用欧几里得距离(ED)将测试图像特征与数据库图像特征进行比较,计算性能参数。实验结果表明,该算法在性能上优于现有算法。
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COMPRESSION BASED FACE RECOGNITION USING DWT AND SVM
The biometric is used to identify a person effectively and employ in almost all applications of day to day activities. In this paper, we propose compression based face recognition using Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM). The novel concept of converting many images of single person into one image using averaging technique is introduced to reduce execution time and memory. The DWT is applied on averaged face image to obtain approximation (LL) and detailed bands. The LL band coefficients are given as input to SVM to obtain Support vectors (SV’s). The LL coefficients of DWT and SV’s are fused based on arithmetic addition to extract final features. The Euclidean Distance (ED) is used to compare test image features with database image features to compute performance parameters. It is observed that, the proposed algorithm is better in terms of performance compared to existing algorithms .
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