计算机工程与设计
計算機工程與設計
계산궤공정여설계
COMPUTER ENGINEERING AND DESIGN
2014年
12期
4302-4305,4310
,共5页
人脸识别%主成分分析%独立成分分析%支持向量机%粒子群算法
人臉識彆%主成分分析%獨立成分分析%支持嚮量機%粒子群算法
인검식별%주성분분석%독립성분분석%지지향량궤%입자군산법
face recognition%principal component analysis (PCA)%independent component analysis (ICA)%support vector ma-chine (SVM)%particle swarm optimization (PSO)
针对人脸识别中，利用粒子群算法训练支持向量机进行分类识别时存在易陷入局部最优和收敛速度慢的问题，提出一种基于雁群优化算法的人脸识别方法。将主成分分析与独立成分分析相结合提取人脸特征，利用支持向量机进行分类，在分类识别的过程中，引入雁群优化算法以提高速度和效率。实验结果表明，与标准粒子群算法相比，改进的粒子群算法提高了人脸识别率，具有较快的识别速度。
針對人臉識彆中，利用粒子群算法訓練支持嚮量機進行分類識彆時存在易陷入跼部最優和收斂速度慢的問題，提齣一種基于雁群優化算法的人臉識彆方法。將主成分分析與獨立成分分析相結閤提取人臉特徵，利用支持嚮量機進行分類，在分類識彆的過程中，引入雁群優化算法以提高速度和效率。實驗結果錶明，與標準粒子群算法相比，改進的粒子群算法提高瞭人臉識彆率，具有較快的識彆速度。
침대인검식별중，이용입자군산법훈련지지향량궤진행분류식별시존재역함입국부최우화수렴속도만적문제，제출일충기우안군우화산법적인검식별방법。장주성분분석여독립성분분석상결합제취인검특정，이용지지향량궤진행분류，재분류식별적과정중，인입안군우화산법이제고속도화효솔。실험결과표명，여표준입자군산법상비，개진적입자군산법제고료인검식별솔，구유교쾌적식별속도。
Because the particle swarm optimization may easily fall into local optimum and takes long runtime while training sup‐port vector machine as a classifier for human face recognition ,a geese swarm optimization algorithm was proposed .The principle component analysis and the independent component analysis were combined to extract human face features ,and the support vec‐tor machine was used as a classifier .To achieve higher speed and recognition accuracy ,the geese swarm optimization algorithm was introduced in the classification stage .The experimental results show that the human face recognition system using the im‐proved particle swarm optimization algorithm was improved in not only the recognition rate but also the efficiency compared with the standard particle swarm optimization .
