本文選題：人耳識別 + 非受控場景　； 參考：《北京科技大學》2017年博士論文

【摘要】：人耳識別是最常見的生物特征識別技術(shù)之一,具有不受表情影響、不受年齡影響、無需被采集對象配合、可遠距離完成等優(yōu)點。魯棒的人耳識別系統(tǒng)在諸多方面都有巨大的應用前景,例如門禁管理、出入境管理、法律實施和刑事偵查等方面。經(jīng)過學術(shù)界多年的研究,人耳識別技術(shù)已經(jīng)取得了長足的進步。但是,在學術(shù)界將研究重點放在如何提高識別率的同時,一些潛在的根源問題卻被忽略了。其中,單樣本問題就是亟待解決的問題之一。單樣本問題是在現(xiàn)實應用中經(jīng)常會遇到的,例如抓捕無犯罪前科的嫌疑犯時,公安機關(guān)所掌握的可能僅有其一幅證件照片或監(jiān)控截圖。從本質(zhì)上來看,單樣本問題并不僅僅是訓練樣本數(shù)量的問題,而是已知的個體信息不能完全包括待識別樣本各種情況下的信息,也就是信息不對稱的問題。在非受控場景下,就歸結(jié)為由姿態(tài)、遮擋等情況導致的部分數(shù)據(jù)問題。針對這樣的問題,本文進行了以下三個部分的研究:1)提出一種加權(quán)多關(guān)鍵點稀疏表示分類方法(WMKD-SRC:Weighted Multikeypoint Descriptor Sparse Representation-based Classification),用于單樣本人耳識別。通過對待識別樣本的所有關(guān)鍵點自動施加自適應的權(quán)重,進而減小了同一類樣本間的類內(nèi)差別。實驗結(jié)果表明,該方法能夠提高非受控場景下單樣本人耳識別的識別率,尤其是在存在遮擋和姿態(tài)變化的情況下,能夠取得更好的魯棒性。2)為了增大原型庫中各類之間的類間差別,提出一種局部特征權(quán)重優(yōu)化方法(WOLF:Weight Optimization of Local Features)。該方法基于群集智能的經(jīng)典算法,對原型庫中各個樣本的局部特征的權(quán)重進行優(yōu)化計算,最終作用于上一部分的識別方法中。實驗結(jié)果表明,經(jīng)過權(quán)重優(yōu)化后的方法對姿態(tài)變化體現(xiàn)出了更好的魯棒性。3)為了進一步提高單樣本人耳識別的性能,提出一種二維三維數(shù)據(jù)的決策層融合方法(Hybrid MKD-SRC:Hybrid Multikeypoint Descriptor Sparse Representation-based Classification)和一種二維三維數(shù)據(jù)的特征層融合方法(TDSIFT:Texture and Depth Scale Invariant Feature Transform)。決策層的融合方法利用由二維紋理圖和由三維深度圖所獲取的信息在同一框架下進行識別,而特征層的融合方法則是提出一種融合二維紋理圖和三維深度圖的局部特征描述子。實驗結(jié)果表明,兩種方法能夠有效地提高單樣本人耳識別的識別率,并且與其它方法相比,縮短了計算時間。本文的研究不僅對解決非受控場景下的單樣本人耳識別具有重要的研究意義,而且對于類似情況下的其他生物特征識別研究也具有參考和借鑒價值。本文提出的識別方法對于解決現(xiàn)實應用中諸如涉密安保、司法認證、公安機關(guān)破獲刑事案件等方面具有理論指導意義。
[Abstract]:Ear recognition is one of the most common biometric recognition techniques. It is not affected by facial expression, is not affected by age, does not need to be collected to cooperate, and can be completed from a long distance. Robust ear recognition system has great application prospects in many aspects, such as access control, entry and exit management, law enforcement and criminal investigation. After years of academic research, ear recognition technology has made great progress. However, while the academic community focuses on how to improve the recognition rate, some potential root problems are ignored. Among them, the single sample problem is one of the problems to be solved. The problem of single sample is often encountered in practical application, for example, when arresting a suspect with no criminal record, the public security organ may have only one document photograph or surveillance screenshot. In essence, the problem of single sample is not only the problem of the number of training samples, but also the problem that the known individual information can not include all kinds of information in the case of the sample to be identified, that is, the problem of information asymmetry. In an uncontrolled scenario, it can be attributed to some data problems caused by posture and occlusion. In order to solve this problem, this paper presents a weighted multi-key point sparse representation classification method, WMKD-SRC: weighted Multikeypoint Descriptor Sparse Representation-based classification, which is studied in the following three parts: 1) for single sample human ear recognition. By automatically applying adaptive weights to all the key points of the identification samples, the intra-class differences between the same samples are reduced. The experimental results show that this method can improve the recognition rate of ear recognition of uncontrolled scene samples, especially in the presence of occlusion and posture change. In order to increase the differences between classes in the prototype library, a local feature weight optimization method is proposed. Based on the classical algorithm of cluster intelligence, this method optimizes the weights of local features of each sample in the prototype library, and finally acts on the recognition method of the previous part. The experimental results show that the weight optimization method shows better robustness to attitude change. 3) in order to further improve the performance of single sample human ear recognition, This paper presents a decision level fusion method for 2D 3D data, Hybrid MKD-SRC:Hybrid Multikeypoint Descriptor Sparse Representation-based Classification, and a feature layer fusion method for 2D 3D data, which is TDSIFT: and Depth Scale Invariant Feature transform. The method of decision level fusion uses the information obtained from 2D texture image and 3D depth map to be recognized under the same frame, while the fusion method of feature layer is to propose a local feature descriptor for fusion of 2D texture image and 3D depth map. The experimental results show that the two methods can effectively improve the recognition rate of single sample human ear and shorten the computing time compared with other methods. The research in this paper not only has important significance to solve the problem of single sample ear recognition in uncontrolled scenarios, but also has reference value for other biometric recognition in similar situations. The identification method proposed in this paper is of theoretical significance in solving practical applications such as security, judicial authentication, and the detection of criminal cases by public security organs.
【學位授予單位】：北京科技大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TP391.41

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