基于蟻群智能和支持向量機(jī)的人臉性別分類方法
Gender Classification Based on Ant Colony and SVM for Frontal Facial Images
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摘要: 蟻群優(yōu)化算法是根據(jù)自然界中螞蟻能夠?qū)⑹澄镆宰疃搪窂桨峄叵伋策@一智能行為而提出的一種新穎的進(jìn)化算法，該算法不僅具有很好的魯棒性，良好的正反饋特性，而且具有并行分布計(jì)算的特點(diǎn)。同時(shí)，支持向量機(jī)又是一種基于結(jié)構(gòu)風(fēng)險(xiǎn)最小化原理的機(jī)器學(xué)習(xí)技術(shù)，具有很強(qiáng)的學(xué)習(xí)泛化能力，為此，文章提出了基于蟻群優(yōu)化算法和支持向量機(jī)的人臉性別分類的方法。首先，通過(guò)KL變換降低人臉性別特征的維數(shù)，并根據(jù)特征值按照從大到小的順序進(jìn)行排列，然后采用10-交叉確認(rèn)技術(shù)，用蟻群優(yōu)化算法對(duì)人臉性別特征面進(jìn)行選擇，以對(duì)支持向量機(jī)進(jìn)行學(xué)習(xí)、訓(xùn)練和測(cè)試。實(shí)驗(yàn)表明，與其他分類算法相比較，這種方法不僅圖像處理簡(jiǎn)單，實(shí)用性強(qiáng)，而且正確識(shí)別率特別高。
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關(guān)鍵詞:
- 性別分類; 支持向量機(jī); 蟻群優(yōu)化算法; KL變換
Abstract: Ant Colony Optimization (ACO) is a novel evolutionary algorithm derived from the foraging behavior of real ants of nature, which can find the shortest path between a food source and their nest. The main characteristics of ACO are robustness, positive feedback and distributed computation. And at the same time, Support Vector Machine (SVM), based on structure risk minimization principle, has the better performance and the better generalization ability. According to these, a gender classification using SVM and ACO is presented. Firstly, to reduce the dimensionality of the face images, the principal component coefficients of all images are calculated through Karhunen Loeve transform. Then, the eigenvectors are sorted in the descending order of eigenvalues. Secondly, ACO decides which eigenvectors will be used. After ACOs feature selection, the SVMs are trained and tested for gender classification. Deserving the best optimal features with highest accuracy rate, the next validation is continued until 10-fold cross-validations are completed. The experiments indicate that the proposed gender classification system based on ACO and SVM is more practical and efficient in comparison with others. -
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