摘要
为提高基于稀疏表示人脸识别的速度和抗噪性能,研究了交叉花束(CAB)模型及压缩感知重构算法。针对重构算法中的大矩阵求逆,提出快速正交匹配追踪(FOMP)算法,可将运算量较高的矩阵求逆运算转变为轻量级向量矩阵运算。为增加高噪声图片的有效信息量,提出几种实用且有效的方法,并通过实验验证这些方法都能提高高噪声人脸识别率,可识别的噪声比例提高到75%,具有一定的实用价值。
To improve the speed and anti-noise performance of face recognition based on sparse representation, the Cross- And-Bouquet （CAB） model and Compressed Sensing （CS） reconstruction algorithm were studied. Concerning the large matrix inversion of reconstruction algorithm, a Fast Orthogonal Matching Pursuit （FOMP） algorithm was proposed. The proposed algorithm could convert the high complexity operations of matrix inversion into the lightweight operation of vector-matrix computation. To increase the amount of effective information in dense noise pictures, several practical and efficient methods were put forward. The experimental results verify that these methods can effectively improve the face recognition rate in dense noise cases, and identifiable noise ratio can reach up to 75%. These methods are of oractical values.
出处
《计算机应用》
CSCD
北大核心
2012年第8期2313-2315,2319,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(60870010
61065003)
关键词
压缩感知
稀疏表示
人脸识别
贪婪匹配追踪算法
过完备字典
Compressed Sensing （CS）
sparse representation
face recognition
Orthogonal Matching Pursuit （OMP）algorithm
overcomplete dictionary
