本文關鍵詞： 人臉識別 人耳識別 多模態(tài)識別 點云配準 稀疏表示　出處：《北京科技大學》2015年博士論文　論文類型：學位論文

【摘要】：隨著信息化社會的不斷發(fā)展,信息安全成為社會關注的熱點,基于生物特征的身份識別在社會生活中的需求越來越強烈。近年開展的生物特征識別研究工作已經(jīng)表明,單一模態(tài)的生物特征識別在實際應用中的準確性和魯棒性難以滿足需要,多模態(tài)識別可以融合多種生物特征體,豐富個體的鑒別信息,提高識別的準確性和魯棒性。以人臉和人耳兩種生物模態(tài)進行融合的識別具有友好性和非打擾性等特點,成為多模態(tài)生物識別研究的熱點之一。 受益于三維數(shù)據(jù)采集技術的發(fā)展,生物特征識別領域中的相當一部分研究延伸到使用三維信息進行識別。相對于二維識別,三維識別對光照和姿態(tài)變化的魯棒性有所提高,但仍有受表情變化的影響明顯、三維數(shù)據(jù)存儲和計算開銷大等不足,另外,三維識別同樣面臨著遮擋和數(shù)據(jù)缺失的問題。在非受控識別場景中,姿態(tài)、遮擋等帶來的影響使得獲取的個體生物特征數(shù)據(jù)在多數(shù)情況下是部分的,存在不可控的變化、缺失,因此實際場景下的識別往往是利用部分數(shù)據(jù)所進行的識別,如何利用部分數(shù)據(jù)來進行身份識別是生物特征識別要解決的典型核心問題之一。 為實現(xiàn)更為魯棒的身份識別,克服單一模態(tài)識別的不足,本文通過球面變換將采集到的人臉人耳三維數(shù)據(jù)轉換為以識別對象為中心進行表達,進而生成多模態(tài)人臉人耳球面深度圖與球面紋理圖(MARS圖)。MARS圖自然融合了人臉人耳兩種模態(tài),包含了更完整的結構信息和紋理信息,有助于克服人臉、人耳單模態(tài)識別中姿態(tài)、遮擋、表情等問題帶來的影響。MARS圖消除了平面外旋轉,能夠實現(xiàn)無需對準的識別,其二維表達形式可減少數(shù)據(jù)存儲開銷,降低識別過程的計算復雜度。鑒于非受控場景下中的身份識別往往是利用部分數(shù)據(jù)所進行的識別,因此本文重點研究非受控場景下基于部分數(shù)據(jù)來進行識別的方法。在注冊階段,通過多視角三維人臉人耳數(shù)據(jù)的融合,構建注冊時相對更為完整的全景MARS圖原型庫來表達身份信息;在識別階段,構建單視角MARS圖并提取單視角MARS圖的局部特征與原型庫中的全景MARS圖局部特征匹配,進行多任務稀疏表示識別。 本文的主要研究內容和創(chuàng)新點包括：第一,研究把人臉人耳三維數(shù)據(jù)由以采集設備為中心的表達轉換為以識別對象為中心進行表達的方法,提出了MARS圖的數(shù)據(jù)表達方法,降低了存儲和計算開銷,有助于實現(xiàn)非受控場景下無需數(shù)據(jù)對準的身份識別。第二,研究三維人臉人耳定位提取方法以及非剛性部分重合的多視角數(shù)據(jù)融合方法,提出了基于膚色檢測的純人臉人耳提取算法和基于BANICP的點云配準方法,實現(xiàn)人臉人耳的自動提取和非剛性人臉人耳點云的多視角數(shù)據(jù)配準和融合。第三,針對非受控場景下基于部分數(shù)據(jù)的身份識別問題,提出了基于MARS圖仿射SIFT特征的多任務稀疏表示識別算法(ASMSRC:Affine-Sift based Multitask Sparse Represent Classifica-tion),通過多任務稀疏表示字典的構建和多任務最優(yōu)稀疏表示系數(shù)求解,對測試樣本的局部特征進行重構,依據(jù)平均重構誤差進行分類和識別。 本文提出的基于MARS圖的人臉人耳多模態(tài)識別方法同時融合了結構特征和紋理特征,對光線變化、姿態(tài)變化、部分遮擋和表情變化具有較強的魯棒性,很大程度上解決了非受控場景下基于部分數(shù)據(jù)匹配的身份識別問題。本文的研究不僅對基于人臉人耳的身份識別,而且對更廣泛領域中的應用基礎和理論研究都是有意義的。
[Abstract]:With the continuous development of information society, information security has become the focus of the society, based on biometric identification needs in the social life of the increasingly strong. Biometrics research work carried out in recent years have shown that the single modal biometric recognition in practical application, the accuracy and robustness of the difficult to meet the needs of multi modal identification can the integration of a variety of biological characteristics, rich individual identification information, to improve the recognition accuracy and robustness. The face and ear of two biological modal fusion recognition has the characteristics of friendly and non intrusive, becomes one of the hot research of multimodal biometrics.
Benefiting from the development of three-dimensional data acquisition technology, biometric identification technology is part of the study is extended to identify the use of three-dimensional information. Compared with two-dimensional recognition, robust 3D recognition of illumination and pose changes has increased, but there are still affected by the expression was affected obviously, lack of three-dimensional data storage and computing cost etc. in addition, 3D recognition also faces occlusion and the problem of missing data. The attitude in non controlled recognition in the scene, and the occlusion caused by making the individual biometric data obtained in most cases is part of the existence, change, uncontrollable loss, therefore the recognition scenario is often identified using part of the data, how to use the data for identification is one of the core issues of typical biometric identification to solve.
In order to achieve more robust identification, to overcome the shortcomings of single modal identification, the spherical transform 3D face data acquisition to the human ear to convert to the recognition object as the center of expression, and then generate the multimodal face and ear spherical depth map and spherical texture map (MARS map).MARS map of natural fusion of face two kinds of ear mode, contains the structure information and the texture information is more complete, helps to overcome the human face, gesture, ear recognition of single mode occlusion, bring expression and so on.MARS map to eliminate the plane rotation, to achieve recognition without the alignment, the two-dimensional expression can reduce data storage overhead. To reduce the computational complexity of the recognition process. In view of the identification of uncontrolled scenarios is often in recognition by using part of the data, so this paper focuses on the research of non controlled scenarios of data based on. The method for identification. In the registration phase, through the fusion of multi view 3D face and ear data, construct the registration relatively more complete panoramic MARS prototype library to express identity information; at the recognition stage, construction of single view MARS map and MARS map extraction panoramic local features of the local feature and the prototype of single view MARS map in matching, multi task sparse representation recognition.
The main research contents and innovations include: first, the research method of face and ear by expression of 3D data acquisition equipment for conversion to the center is to identify the object as the center of the expression of the proposed MARS map data expression method, reduce the storage and computation overhead, and identity recognition helps to realize the non controlled scene without data alignment. Second fusion localization method based on 3D face extraction method of human ear and multi view data coincide with non rigid part of the proposed facial skin detection pure ear extraction algorithm and registration method based on point cloud BANICP based on the realization of the human ear face automatic extraction and non rigid face and ear point cloud multi view data registration and fusion. In third, for the non controlled scene based on the identification problem of data in the proposed MARS map affine SIFT feature recognition based on multi task sparse representation Don't (ASMSRC:Affine-Sift based Multitask Sparse Represent algorithm, Classifica-tion) by multi task sparse dictionary construction and multi task optimal sparse representation coefficients, the local characteristics of the test samples to reconstruct, classification and identification based on the average reconstruction error.
The proposed multimodal face recognition method based on MARS graph and combines ear structure feature and texture feature of light change, attitude change, has strong robustness to occlusion and facial expression changes, largely solves the identification problem based on partial data, non controlled scene. This study not only the identity recognition based on face and ear, and the application of a broader base in the field of theory and research are meaningful.

【學位授予單位】：北京科技大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TP391.41

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