基于膚色模型的人臉檢測及特征點定位方法研究
發(fā)布時間：2018-09-12 14:27
【摘要】：人臉檢測及特征點定位在計算機視覺研究中有著重要地位,在生活中應用也極其廣泛。人臉檢測是指在輸入圖像中檢測是否存在人臉,如果存在,則標識出人臉區(qū)域的位置。特征點定位則是在人臉檢測的基礎上,更精確地尋找到臉部的特征點位置。人臉檢測及特征點定位是很多人臉相關應用如表情識別、姿態(tài)估計、人臉動畫合成中的關鍵步驟,這兩個步驟的性能對其后續(xù)人臉相關應用有重要影響。傳統的人臉檢測及特征點定位方法在訓練階段需要進行復雜的特征提取,且魯棒性不佳。本論文致力于簡化特征提取環(huán)節(jié),并構造新的特征點定位優(yōu)化方法,進一步提高檢測和定位的準確性和速度。本文對人臉檢測及特征點定位方法進行了深入研究。首先,將膚色模型和用深度學習方法訓練的人臉分類器結合用于人臉的檢測,在人臉檢測的基礎上,再利用回歸網絡對檢測結果不精確的人臉區(qū)域做回歸處理,以獲得更精準的人臉檢測定位。接下來,根據回歸后的人臉檢測結果進行特征點定位。特征點定位是采用隨機森林方法,通過對人臉特征點建立全局約束模型進行整體優(yōu)化,利用級聯回歸結構迭代獲得人臉特征點的精確位置。論文主要研究內容如下:1、而本文中通過深度學習方法設計并訓練一個人臉分類器網絡,將其與膚色模型相結合,能夠更有效地檢測出復雜場景下的人臉,且避免了顯式的特征設計和提取環(huán)節(jié)。2、針對前一步驟人臉檢測環(huán)節(jié)中可能出現的檢測精度不高的情況,設計了一個回歸網絡,利用回歸網絡對檢測結果進行回歸校正以獲得更精確的檢測定位。3、特征點定位初始階段將根據人臉檢測結果給特征點賦予初始坐標,因此,一個精確的人臉檢測結果對于提升特征點定位的速度和精度有重要作用。本文首先訓練隨機森林模型實現對特征點定位重要特征的篩選,之后對人臉特征點建立全局約束模型,用最小二乘方法對全局模型參數進行整體優(yōu)化,最后利用級聯回歸結構進行迭代獲得人臉特征點的精準定位。實驗結果表明,改進的人臉檢測及特征點定位系統能夠有效提高復雜環(huán)境下人臉檢測及特征點定位的性能,在保證魯棒性及較高定位精度的前提下,還擁有接近實時的較高檢測及定位速度。
[Abstract]:Face detection and feature location play an important role in computer vision research and are widely used in daily life. Face detection is to detect the presence of a face in an input image and, if so, to identify the location of the face region. On the basis of face detection, feature point location is more accurately located. Face detection and feature location are the key steps in many human face related applications such as facial expression recognition, pose estimation and face animation synthesis. The performance of these two steps plays an important role in the subsequent face related applications. The traditional face detection and feature point localization methods need complex feature extraction in the training stage, and the robustness is not good. This paper is devoted to simplify feature extraction and construct a new method of feature location optimization to further improve the accuracy and speed of detection and location. In this paper, the methods of face detection and feature point location are studied. Firstly, the skin color model and the face classifier trained by the depth learning method are combined for face detection. Then, based on the face detection, the regression network is used to deal with the face region with inaccurate detection results. In order to obtain more accurate face detection location. Then, the feature points are located according to the result of face detection after regression. The feature point location is based on the stochastic forest method. The global constraint model is established for the face feature points and the exact location of the face feature points is obtained by cascading regression structure iterations. The main contents of this paper are as follows: 1. In this paper, a human face classifier network is designed and trained by the deep learning method, which is combined with the skin color model to detect the human face in the complex scene more effectively. And avoid the explicit feature design and extraction link. 2. Aiming at the situation that the detection accuracy may not be high in the previous step face detection link, a regression network is designed. Regression network is used to correct the detection results to obtain more accurate detection location. The initial phase of feature point location will be assigned to the initial coordinates of feature points according to the face detection results, so, An accurate face detection result plays an important role in improving the speed and accuracy of feature point location. In this paper, we first train the stochastic forest model to select important features for feature point location, and then establish a global constraint model for facial feature points, and optimize the global model parameters by using the least square method. Finally, a cascade regression structure is used to iterate to obtain accurate location of face feature points. The experimental results show that the improved face detection and feature point location system can effectively improve the performance of face detection and feature point location in complex environments, while ensuring robustness and high positioning accuracy. It also has high detection and positioning speed near real time.
【學位授予單位】：重慶理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
本文編號：2239317
[Abstract]:Face detection and feature location play an important role in computer vision research and are widely used in daily life. Face detection is to detect the presence of a face in an input image and, if so, to identify the location of the face region. On the basis of face detection, feature point location is more accurately located. Face detection and feature location are the key steps in many human face related applications such as facial expression recognition, pose estimation and face animation synthesis. The performance of these two steps plays an important role in the subsequent face related applications. The traditional face detection and feature point localization methods need complex feature extraction in the training stage, and the robustness is not good. This paper is devoted to simplify feature extraction and construct a new method of feature location optimization to further improve the accuracy and speed of detection and location. In this paper, the methods of face detection and feature point location are studied. Firstly, the skin color model and the face classifier trained by the depth learning method are combined for face detection. Then, based on the face detection, the regression network is used to deal with the face region with inaccurate detection results. In order to obtain more accurate face detection location. Then, the feature points are located according to the result of face detection after regression. The feature point location is based on the stochastic forest method. The global constraint model is established for the face feature points and the exact location of the face feature points is obtained by cascading regression structure iterations. The main contents of this paper are as follows: 1. In this paper, a human face classifier network is designed and trained by the deep learning method, which is combined with the skin color model to detect the human face in the complex scene more effectively. And avoid the explicit feature design and extraction link. 2. Aiming at the situation that the detection accuracy may not be high in the previous step face detection link, a regression network is designed. Regression network is used to correct the detection results to obtain more accurate detection location. The initial phase of feature point location will be assigned to the initial coordinates of feature points according to the face detection results, so, An accurate face detection result plays an important role in improving the speed and accuracy of feature point location. In this paper, we first train the stochastic forest model to select important features for feature point location, and then establish a global constraint model for facial feature points, and optimize the global model parameters by using the least square method. Finally, a cascade regression structure is used to iterate to obtain accurate location of face feature points. The experimental results show that the improved face detection and feature point location system can effectively improve the performance of face detection and feature point location in complex environments, while ensuring robustness and high positioning accuracy. It also has high detection and positioning speed near real time.
【學位授予單位】：重慶理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
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