【摘要】：人臉識別技術(shù)是計算機(jī)視覺和模式識別領(lǐng)域的重要研究內(nèi)容之一。由于人臉識別問題的復(fù)雜性,且受到諸多外在因素的影響,要在識別率與穩(wěn)定性上達(dá)到較高的標(biāo)準(zhǔn)比較困難。本文提出的人臉識別方法首先用級聯(lián)回歸算法對人臉關(guān)鍵特征點進(jìn)行精確定位,用定位好的關(guān)鍵特征點對人臉進(jìn)行校正,求取關(guān)鍵特征點局部鄰域內(nèi)的LBP直方圖統(tǒng)計特征進(jìn)行人臉建模,最后采用SVM分類器對人臉模型進(jìn)行分類與識別。本文的人臉識別方法有效提高了人臉識別的準(zhǔn)確率和識別效率,在GT人臉數(shù)據(jù)庫上進(jìn)行的實驗中看出,本文人臉識別方法識別率最高可以達(dá)到99%。本文主要研究工作包括:(1)深入研究基于級聯(lián)回歸算法的人臉定位。級聯(lián)回歸算法采用迭代回歸求解的方式優(yōu)化人臉定位的準(zhǔn)確性,本文在LBF特征的訓(xùn)練過程中,采用三種閾值計算方法改進(jìn)了RF算法中弱回歸樹節(jié)點的訓(xùn)練。在LFPW數(shù)據(jù)集上的訓(xùn)練和測試證實改進(jìn)算法能有效提高LBF特征的穩(wěn)定性,提升模型整體的預(yù)測精度。(2)提出局部LBP人臉建模算法。全臉LBP人臉建模割裂了特征之間的關(guān)系且引入較多噪聲。本文提出的局部LBP人臉建模算法,在降低噪聲的同時保留了人臉的關(guān)鍵信息,為人臉識別器的訓(xùn)練提供了更好的特征數(shù)據(jù)。(3)構(gòu)建基于SVM的人臉識別器。相比于直接的歐氏距離計算,SVM算法可以從人臉建模數(shù)據(jù)中找到關(guān)鍵區(qū)分樣本,不僅識別效果更好,而且提高了時間上的響應(yīng)效率。
[Abstract]:Face recognition technology is one of the important research contents in the field of computer vision and pattern recognition. Due to the complexity of face recognition and the influence of many external factors, it is difficult to achieve high recognition rate and stability. The method of face recognition proposed in this paper firstly uses cascaded regression algorithm to accurately locate the key feature points of the face, and corrects the face with the key feature points of good location. The LBP histogram statistical features in the local neighborhood of the key feature points are used to model the face. Finally, the SVM classifier is used to classify and recognize the face model. The method of face recognition in this paper can effectively improve the accuracy and efficiency of face recognition. The experiments on GT face database show that the recognition rate of this method can reach 99%. The main work of this paper is as follows: (1) the face localization based on cascade regression algorithm is studied in depth. The concatenated regression algorithm optimizes the accuracy of face location by iterative regression solution. In this paper, three threshold calculation methods are used to improve the training of weak regression tree nodes in RF algorithm during the training process of LBF features. Training and testing on LFPW datasets show that the improved algorithm can effectively improve the stability of LBF features and improve the overall prediction accuracy of the model. (2) A local LBP face modeling algorithm is proposed. Full-face LBP face modeling splits the relationship between features and introduces more noise. The local LBP face modeling algorithm proposed in this paper not only reduces the noise but also preserves the key information of the face and provides better feature data for the training of the face recognizer. (3) A face recognizer based on SVM is constructed. Compared with the direct Euclidean distance calculation, the SVM algorithm can find the key discriminant samples from the face modeling data, which not only has better recognition effect, but also improves the response efficiency in time.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王李冬;;一種新的人臉識別算法[J];計算機(jī)技術(shù)與發(fā)展;2009年05期

2 桂榮;沈榮鑫;詹泳;;基于模糊神經(jīng)推理的人臉識別算法研究[J];華南師范大學(xué)學(xué)報(自然科學(xué)版);2012年04期

3 蘇士美;王燕;王明霞;;基于加權(quán)小波分解的人臉識別算法研究[J];鄭州大學(xué)學(xué)報(工學(xué)版);2014年01期

4 魏冬梅;周衛(wèi)東;;采用壓縮感知的人臉識別算法[J];計算機(jī)工程;2011年18期

5 陳皓;霍星;;視頻監(jiān)控中人臉識別算法穩(wěn)定性的改進(jìn)[J];工程圖學(xué)學(xué)報;2011年06期

6 李美瑾;;人臉識別算法綜述與遠(yuǎn)程考試系統(tǒng)應(yīng)用研究[J];廣西廣播電視大學(xué)學(xué)報;2012年01期

7 盛朝強(qiáng);王君;;煤礦井下人員簽到系統(tǒng)人臉識別算法研究[J];計算機(jī)技術(shù)與發(fā)展;2012年07期

8 朱學(xué)毅;王崇駿;周新民;張W，

本文編號：2282324