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  本文選題：人臉特征點定位　切入點：非可控場景　出處：《北京交通大學》2017年碩士論文　論文類型：學位論文

【摘要】：人臉特征點定位是自動定位事先定義好的面部關鍵點,以獲取人臉形狀。人臉特征點定位是人臉圖像分析的關鍵步驟,在人臉識別、仿真、跟蹤、表情分析、人臉三維動畫建模等任務中有著廣泛的應用,受到研究者們廣泛的關注。目前,在可控環(huán)境下,人臉特征點定位和人臉分析相關任務已經(jīng)達到了比較滿意的結果。但在非控場景下,由于受到光照、姿態(tài)、表情、遮擋等非可控因素的影響,人臉外觀和形狀變化呈現(xiàn)高度非線性。因此,已有模型在人臉特征點定位和人臉其他相關任務上的性能依然很差,無法滿足實際應用的要求。本論文主要針對非可控場景下的人臉特征點定位問題進行研究,并針對該問題提出了相應的解決辦法。主要創(chuàng)新點如下:(1)在非可控場景下,人臉外觀變化呈現(xiàn)高度非線性,而現(xiàn)有模型的視覺特征描述方法難以對其進行準確表達。為了解決該問題,本文提出一種基于深度卷積特征和極限學習機的人臉特征點定位方法。該方法包括:首先,設計并訓練深度卷積神經(jīng)網(wǎng)絡,提取全局卷積特征,包含空間像素和上下文語義信息;然后,引入魯棒的極限學習機,代替卷積神經(jīng)網(wǎng)絡中內(nèi)置的回歸器,實現(xiàn)人臉外觀特征到人臉形狀的非線性映射;最后,融合多尺度的預測結果確定特征點位置。實驗結果表明:包含空間像素和上下文語義信息的卷積特征凸顯了復雜人臉外觀的一般模式,更有助于定位任務;極限學習機回歸函數(shù)對人臉外觀特征到人臉形狀之間的映射能力較強;融合多尺度預測可以進一步提高定位的精度。(2)在非可控場景下,人臉外觀和人臉形狀存在較大的差異,而現(xiàn)有的級聯(lián)形狀回歸模型對初始形狀敏感,局部特征忽略形狀約束信息。為了解決該問題,本文提出一種改進的級聯(lián)回歸模型實現(xiàn)人臉特征點定位。該模型包括:首先,在級聯(lián)結構的第一級,通過學習算法直接輸出所有特征點位置作為初始形狀,代替人工賦值的初始形狀,在級聯(lián)結構的后幾級,設計并訓練多目標的淺層卷積神經(jīng)網(wǎng)絡,提取局部卷積特征,包含局部相關性信息;然后,改進極限學習機回歸函數(shù)的優(yōu)化方法,提高算法的泛化性;最后,通過級聯(lián)框架實現(xiàn)由粗到精的人臉特征點定位。實驗結果表明:通過學習算法初始人臉形狀魯棒性較高;增加局部特征之間的相關性可以添加形狀約束信息,提高定位的精度。另外,本文提出的級聯(lián)形狀回歸模型每級基于不同類型的特征,拓寬了現(xiàn)有的級聯(lián)形狀回歸方法。
[Abstract]:Face feature point localization is the key point of the face that is defined in advance to obtain the shape of the face. Face feature point location is the key step of face image analysis, in face recognition, simulation, tracking, expression analysis, face recognition, simulation, tracking, facial expression analysis, Human face 3D animation modeling and other tasks have been widely used by researchers. At present, in a controllable environment, The tasks related to face feature location and face analysis have achieved satisfactory results. However, in the non-controlled scene, due to the influence of uncontrollable factors such as illumination, pose, facial expression, occlusion, etc. Face appearance and shape change are highly nonlinear. Therefore, the performance of existing models in facial feature location and other related tasks is still very poor. This paper mainly focuses on the problem of face feature point localization in uncontrollable scene, and puts forward the corresponding solution. The main innovation is as follows: 1) in the uncontrollable scene, The appearance change of human face is highly nonlinear, but it is difficult to express it accurately by the visual feature description methods of the existing models. In order to solve the problem, In this paper, a face feature point localization method based on deep convolution feature and extreme learning machine is proposed. The method includes: firstly, the deep convolution neural network is designed and trained to extract global convolution feature. It contains spatial pixels and contextual semantic information. Then, a robust extreme learning machine is introduced to replace the built-in regression in convolution neural network to realize the nonlinear mapping from facial appearance to face shape. The experimental results show that the convolution feature, which contains spatial pixels and context semantic information, highlights the general pattern of complex face appearance, and is more helpful to localization task. The regression function of extreme learning machine has a strong ability to map the appearance features to the shape of the face, and the fusion of multi-scale prediction can further improve the accuracy of location. (2) in the uncontrollable scene, there are great differences between the appearance of the face and the shape of the face. However, the existing cascade shape regression model is sensitive to initial shape and local features ignore shape constraint information. In order to solve this problem, an improved cascade regression model is proposed to locate facial feature points. In the first stage of cascade structure, all feature points are directly output as initial shape by learning algorithm, instead of the initial shape of artificial assignment. In the later stages of cascade structure, a multi-objective shallow convolution neural network is designed and trained. Extract local convolution features, including local correlation information; then, improve the optimization method of LLM-regression function, improve the generalization of the algorithm; finally, Face feature point localization from coarse to fine is realized by cascading frame. Experimental results show that the initial face shape is robust by learning algorithm, and shape constraint information can be added by increasing the correlation between local features. In addition, the cascade shape regression model proposed in this paper is based on different types of features, which broadens the existing cascade shape regression methods.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關博士學位論文 前1條

1 宋鳳義;非控制條件下的人臉分析與驗證[D];南京航空航天大學;2014年

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本文編號：1559191