本文選題：人臉識(shí)別 + 光照模型��； 參考：《貴州民族大學(xué)》2017年碩士論文

【摘要】：人臉識(shí)別技術(shù)作為身份識(shí)別的主要內(nèi)容,是目前數(shù)字圖像處理及模式識(shí)別研究的熱點(diǎn)問(wèn)題。其通過(guò)計(jì)算機(jī)分析采集到的人臉圖像,提取出有效的識(shí)別信息,用于自動(dòng)“辯認(rèn)”身份,解決“他是誰(shuí)?”的問(wèn)題。但由于人臉圖像采集過(guò)程中,受到復(fù)雜外部環(huán)境和自身屬性的的影響,如光照、陰影、化妝、配戴首飾(頭飾)等的影響,同一人臉獲取的圖像會(huì)有較大的差別,這些使得用機(jī)器來(lái)正確識(shí)別人臉變得艱難。本文從人臉圖像的穩(wěn)定特征分布出發(fā),利用人臉類別分布的先驗(yàn)知識(shí),提出一種自適應(yīng)的人臉圖像標(biāo)準(zhǔn)化方法和光照不變性的特征提取算法,通過(guò)對(duì)這些特征的分析來(lái)實(shí)現(xiàn)對(duì)人臉圖像的識(shí)別。主要工作如下:1、使用改進(jìn)高斯膚色模型進(jìn)行人臉檢測(cè)與分割,在朗伯光照模型、伽馬校正、對(duì)數(shù)變換等圖像處理基礎(chǔ)上結(jié)合同態(tài)濾波,提出了一種自適應(yīng)的人臉圖像標(biāo)準(zhǔn)化方法。2、對(duì)傳統(tǒng)的LBP、多級(jí)LBP、Gabor小波等光照不變性的特征提取算法進(jìn)行了深入的研究,在二維Gabor小波算法基礎(chǔ)上融合LBP思想,獲得降低特征維數(shù)的特征抽取方法。3、設(shè)計(jì)統(tǒng)一標(biāo)準(zhǔn)的分類方法,分別使用最近鄰分類和LibSVM方法進(jìn)行分類識(shí)別。并對(duì)實(shí)驗(yàn)結(jié)果進(jìn)行比較。4、在visual studio 2015平臺(tái)上,將本文算法集成應(yīng)用于高校迎新實(shí)驗(yàn)系統(tǒng),對(duì)新入校的學(xué)生進(jìn)行身份識(shí)別,獲得較好的效果。
[Abstract]:As the main content of identity recognition, face recognition is a hot issue in the research of digital image processing and pattern recognition. Through the computer analysis of the collected face images, extract effective recognition information, for automatic "identification" identity, to solve "who is he?" The question of However, due to the influence of complex external environment and its own attributes, such as illumination, shadow, makeup, wearing jewelry (headdress) and so on, the image acquired by the same face will be quite different in the process of face image acquisition. This makes it difficult to correctly identify human faces with machines. Based on the stable feature distribution of face image and the prior knowledge of face category distribution, an adaptive method of face image standardization and a feature extraction algorithm based on illumination invariance are proposed in this paper. The recognition of face image is realized by analyzing these features. The main work is as follows: 1. Using improved Gao Si skin color model for face detection and segmentation, combining homomorphic filtering with image processing such as Lambert illumination model, gamma correction, logarithmic transformation, etc. In this paper, an adaptive face image standardization method .2is proposed. The traditional feature extraction algorithms such as LBP and multilevel LBP- Gabor wavelet are studied deeply, and the LBP idea is fused on the basis of two-dimensional Gabor wavelet algorithm. The feature extraction method. 3 is obtained to reduce the feature dimension. A unified classification method is designed. The nearest neighbor classification method and the LibSVM method are used for classification and recognition respectively. The experimental results are compared with .4. on the platform of visual studio 2015, the algorithm of this paper is integrated into the experimental system of welcoming new students in colleges and universities, and the identification of new students is carried out, and good results are obtained.
【學(xué)位授予單位】：貴州民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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