發(fā)布時間：2018-03-17 16:05

  本文選題：人臉檢測　切入點：粒子群算法　出處：《新疆大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：人臉富含獨特的生物特征信息,具有唯一性和高可辨識性,使其作為身份認證已廣泛應(yīng)用到智能監(jiān)控、社區(qū)安防、金融支付等生活重要領(lǐng)域。人臉檢測作為人臉圖像信息處理的第一步,能否準確、快速的檢測并標定出一張圖片中人臉的位置、數(shù)量有著重要的意義和研究價值�？萍嫉倪M步致使信息安全不斷受到新技術(shù)的挑戰(zhàn),人們逐漸從傳統(tǒng)的認證方式轉(zhuǎn)向數(shù)字化安全性更高的生物特征認證方式,如指紋、虹膜、人臉等生物特征。其中人臉具有穩(wěn)定性、唯一性,安全性高,難以被復(fù)制,使其作為生物特征識別技術(shù)之一受到越來越多研究者的關(guān)注近年來。隨著模式識別、人工智能等領(lǐng)域的快速發(fā)展人臉檢測的研究也獲得了很大的進步,目前針對正常環(huán)境下的清晰正面人臉已實現(xiàn)準確快速的檢測。但在現(xiàn)實生活中,由于光照、遮擋、復(fù)雜背景等條件的影響導(dǎo)致拍攝到的人臉圖像并不是清晰的正面照,這會給人臉圖像后續(xù)的處理帶來一定的困擾。針對目前人臉檢測算法存在的一些不足之處,為了提升檢測技術(shù)的準確率和魯棒性,本文在深入研究了大量傳統(tǒng)的人臉檢測算法后,提出了相關(guān)的優(yōu)化改進算法。研究內(nèi)容主要有如下:第一、針對在強光、遮擋等復(fù)雜環(huán)境下的人臉圖像,本文提出一種基于改進粒子群(Particle Swarm Optimization,PSO)與K均值聚類膚色分割的人臉檢測方法。該方法首先將待測圖片變換到Y(jié)CgCr彩色空間,利用膚色信息在YCgCr彩色空間分布的集中性,在該色彩空間使用改進的粒子群與K均值聚類綜合的方法進行膚色分割;為有效的去除人臉區(qū)域以外噪聲,得出候選人臉區(qū),需要對分割后得到的膚色區(qū)域使用二值形態(tài)學(xué)和人臉幾何形狀特征處理;最后通過改進的AdaBoost算法對候選人臉區(qū)域進行驗證。經(jīng)實驗結(jié)果顯示,使用本文方法進行人臉檢測正確率較高,同時算法的魯棒性和適應(yīng)性好。第二、針對受光照不均影響的人臉圖像,本文提出一種基于膚色分割和特征定位的多人臉檢測方法。該方法首先在RGB空間檢測圖像是否有色彩偏差,若存在色彩偏差則采用改進的參考白算法進行光照補償;接著將處理后的圖片轉(zhuǎn)換到Y(jié)CbCr顏色空間進行膚色分割,并通過改進的AdaBoost算法進行人臉檢測得到候選區(qū)域;然后采用經(jīng)過大量人眼訓(xùn)練實驗得到的先驗知識在候選人臉區(qū)域標記出人眼;最后輸出帶人眼定位的人臉圖像。
[Abstract]:Face is rich in unique biometric information, unique and highly identifiable, so it has been widely used in intelligent monitoring, community security, as identity authentication, As the first step of face image information processing, whether face detection can accurately, quickly detect and calibrate the position of face in a picture, With the development of science and technology, information security is constantly challenged by new technologies. People are gradually changing from traditional authentication to biometric authentication with higher digital security, such as fingerprint, iris, etc. As one of the biometric recognition techniques, human face has attracted more and more attention in recent years. With the development of pattern recognition, it is difficult to be copied because of its stability, uniqueness, security and so on. In recent years, as one of the biometric recognition techniques, face has attracted more and more attention. The rapid development of face detection in artificial intelligence and other fields has also made great progress. At present, the clear frontal face detection in normal environment has been realized accurately and quickly. But in real life, due to illumination, occlusion, Due to the influence of complex background and other conditions, the face image taken is not clear positive image, which will bring some troubles to the subsequent processing of human face image. There are some shortcomings in the current face detection algorithm. In order to improve the accuracy and robustness of the detection technology, after deeply studying a large number of traditional face detection algorithms, this paper proposes a related optimization improvement algorithm. The main contents of the research are as follows: first, in order to improve the accuracy and robustness of the detection technology, In this paper, a face detection method based on improved particle swarm optimization (PSO) and K-means clustering is proposed for face image detection in complex environments, such as occlusion, etc. The method first transforms the image under test into YCgCr color space. Using the color information in YCgCr color space distribution centrality, the improved particle swarm and K-means clustering method is used to segment the skin color in this color space, in order to effectively remove the noise outside the face region, the candidate face area is obtained. It is necessary to use binary morphology and facial geometry feature processing to segment the skin color region. Finally, the improved AdaBoost algorithm is used to verify the candidate face region. The experimental results show that, The accuracy of face detection is high, and the algorithm is robust and adaptive. Secondly, face images are affected by uneven illumination. In this paper, a multi-face detection method based on skin color segmentation and feature location is proposed. Firstly, the color deviation is detected in RGB space, and if there is color deviation, the improved reference white algorithm is used to compensate the illumination. Then, the processed images are converted to YCbCr color space for skin segmentation, and the candidate regions are obtained by the improved AdaBoost algorithm for face detection. Then the human eyes are marked in the candidate's face area by the prior knowledge obtained from a large number of human eye training experiments. Finally, the human face image with human eye localization is output.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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