本文關(guān)鍵詞：手掌靜脈識別技術(shù)在貴重物品物流中的應(yīng)用研究　出處：《沈陽大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 貴重物品物流 手掌靜脈 特征提取 用戶界面

【摘要】：貴重物品在物流中丟失、損壞、被冒領(lǐng)引起巨額財產(chǎn)損失和賠償糾紛,傳統(tǒng)的身份認(rèn)證方式如身份證、短信、手機號碼、工號、用戶名、密碼等存在容易被丟失、遺忘、復(fù)制及盜用的隱患,已經(jīng)不能滿足貴重物品物流的應(yīng)用需求。由于手掌靜脈特征具有穩(wěn)定性、唯一性、含有豐富的信息量、難被復(fù)制和竊取、采集方式易于接受等獨特的優(yōu)勢,本文采用新型的身份認(rèn)證方式——手掌靜脈識別技術(shù)對貴重物品物流中的客戶、收派員、倉庫管理員進行監(jiān)督、管理,當(dāng)貴重物品丟失、損壞、被冒領(lǐng)時,保證責(zé)任落實到個人。本文利用自建的掌脈圖像數(shù)據(jù)庫,重點圍繞基于子空間的手掌靜脈特征提取算法展開分析和討論,提出基于主成分分析和FISHER線性判別的方法。FLD提取的是最佳分類特征,本文通過PCA降維克服了單獨使用FLD方法時,出現(xiàn)的小樣本問題。此外,提出上述方法的改進算法,識別階段將降維過程提取的PCA特征與最終提取的FLD特征利用加法融合,取得了較好的識別效果。為了得到原始輸入空間中非線性最佳分類特征,并解決小樣本問題,本文提出基于核主成分分析和FISHER線性判別的掌脈特征提取方法,先用KPCA對圖像降維,然后用FLD提取分類特征,最后采用歐式距離完成匹配。仿真結(jié)果表明,與傳統(tǒng)的2DFLD、本文的PCA+FLD、改進的PCA+FLD相比,在不同的特征個數(shù)下,該方法均取得較高的正確識別率96%,識別時間較短,運行速度較快,滿足貴重物品物流的應(yīng)用需求。最后,利用MATLAB GUI編寫了用戶界面,完成從獲取掌脈圖像到匹配結(jié)果的軟件調(diào)試,顯示人機交互界面。仿真結(jié)果表明,此掌脈識別系統(tǒng)安全可靠,將其運用到貴重物品物流中,不僅具有重大的理論意義,而且具有廣闊的應(yīng)用前景。
[Abstract]:Valuables are lost and damaged in logistics, resulting in huge property losses and compensation disputes. Traditional authentication methods such as identity card, SMS, mobile phone number, worker number, user name. Password is easy to be lost, forgotten, copied and stolen hidden danger, can not meet the application needs of valuables logistics. Because of the palm vein features are stable, unique, rich in information. Difficult to be copied and stolen, easy to accept the acquisition of unique advantages, this paper uses a new type of identity authentication-palm vein identification technology to valuables logistics customers, receiving staff. The storekeeper supervises, manages, when the valuables are lost, damaged, is taken, guarantees the responsibility to carry out to the individual. This paper uses the self-built palm pulse image database. Focusing on the analysis and discussion of subspace-based palmar vein feature extraction algorithm, a method based on principal component analysis and FISHER linear discrimination is proposed to extract the best classification features. This paper overcomes the small sample problem when using the FLD method alone by using PCA dimension reduction. In addition, the improved algorithm of the above method is proposed. In the recognition stage, the PCA feature extracted from the dimensionality reduction process is fused with the FLD feature extracted finally, and good recognition effect is obtained. In order to obtain the best nonlinear classification features in the original input space. And to solve the problem of small samples, this paper proposes a palmar pulse feature extraction method based on kernel principal component analysis and FISHER linear discriminant. Firstly, KPCA is used to reduce the dimension of the image, then FLD is used to extract the classification features. Finally, the Euclidean distance is used to complete the matching. The simulation results show that compared with the traditional 2DF LDD, the PCA FLD of this paper, the improved PCA FLD, under different number of features. The method achieves high correct recognition rate 96, short recognition time, fast running speed, and meets the application requirements of valuables logistics. Finally, the user interface is written by MATLAB GUI. The simulation results show that the palmar pulse recognition system is safe and reliable, and it is used in valuables logistics. Not only has the great theoretical significance, but also has the broad application prospect.
【學(xué)位授予單位】：沈陽大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP391.41

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本文編號：1422715