本文選題：特征提取 + 人臉識別�。� 參考：《計算機科學》2017年01期

【摘要】：現有的大多數特征提取算法在提取人臉特征時,容易受到光照等外界因素的影響,從而導致后期人臉識別率下降。而方向梯度直方圖(Histogram of Oriented Gradient,HOG)具有較強的光照魯棒性,能夠很好地減少由光照帶來的干擾,但傳統HOG在計算梯度幅值和方向時只計算水平和垂直方向上4個像素點對中間像素的影響,當外界環(huán)境變化時不能保證穩(wěn)定性,因此提出一種基于Haar特性的改進HOG的人臉特征提取算法。該算法在計算梯度幅值和方向時考慮水平、垂直以及對角線上8個像素點對中間像素的影響,由于增加計算量導致特征提取時間也隨之增加,因此引入Haar,借助Haar型特征運算簡單、快捷的特點設計4組Haar型特征編碼模式,按照改進的HOG特征計算方式提取人臉特征。在有光照等外界因素影響的FERET人臉數據庫和Yale B擴展的人臉測試庫中進行實驗,實驗結果表明,與GFC,LBP和其他文獻中的HOG算法相比,該算法對光照具有更好的魯棒性,能夠在光照變化的環(huán)境下提高人臉識別率。該算法在FERET探測集fb,fc,dup1和dup2上的識別率分別為95.1%,80.9%,70.1%和63.2%,在Yale B中的識別率為89.1%。
[Abstract]:Most of the existing feature extraction algorithms are easy to be affected by external factors such as illumination when extracting face features, which leads to the decline of face recognition rate in the later stage. The histogram of Oriented gradient histogram has strong illumination robustness and can reduce the interference caused by illumination. However, the traditional HOG can only calculate the influence of 4 pixels in horizontal and vertical directions on the intermediate pixels in the calculation of gradient amplitude and direction, and can not guarantee the stability when the external environment changes. Therefore, an improved HOG based face feature extraction algorithm based on Haar characteristics is proposed. When calculating the magnitude and direction of gradient, the algorithm takes into account the influence of 8 pixels on the vertical and diagonal lines on the intermediate pixels, and the time of feature extraction increases with the increase of computation. Therefore, four groups of Haar type feature coding patterns are designed with the help of the simple and fast Haar type feature calculation, and the face features are extracted according to the improved HOG feature calculation method. Experiments are carried out in FERET face database and Yale B extended face test database with external factors such as illumination. The experimental results show that the algorithm is more robust to illumination than HOG algorithm in other literatures. It can improve the face recognition rate in the environment of changing illumination. The recognition rate of this algorithm on the FERET detection set fbbutu dup1 and dup2 is 95.1% and 63.2%, respectively. The recognition rate in Yale B is 89.1.
【作者單位】： 南京郵電大學計算機學院;
【分類號】：TP391.41

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本文編號：1862197