本文選題：人臉美感分析 + 眼動儀�。� 參考：《鄭州大學》2017年碩士論文

【摘要】：自動人臉美感分析是指通過計算機模擬人類對臉部美感的分析與評價機制,并使計算機不斷自主“學習”人類審美方法的一門技術(shù)。近年來,隨著人臉美感評價需求在美容、婚介、招聘、多媒體等不同行業(yè)的不斷增加,自動人臉美感分析技術(shù)得到了迅速發(fā)展。傳統(tǒng)的自動人臉美感分析方法致力于找到能夠影響人臉美感程度的特征和高精度的分類算法,以完成對不同人臉的美感分類與評價。但這些方法在分析圖像信息有效表達的過程中,不能清晰的解釋人類視覺系統(tǒng)和大腦是如何篩選和運用這些特征來進行人臉美感判斷,缺乏科學的方法去統(tǒng)計和驗證其合理性。為了盡可能地還原人類的審美判斷過程,得到更好的分類效果,本文提出了融合生物啟發(fā)和深度屬性學習的人臉美感預測方法,一方面使用基于中層特征表示的方法提高圖像特征提取算法的性能,另一方面使用深度學習和模型融合的方法提高分類算法的精度,最終構(gòu)建出基于深度學習和模型融合的人臉美感分析框架,本文的主要工作分為如下幾個方面:(一)提出了一種基于圖像中層語義的特征提取方法。本方法首先通過眼動儀實驗提取人在審美過程中的人臉顯著性區(qū)域和人臉顯著性權(quán)重矩陣,然后通過“整體+局部”的視覺注意機制獲取決定人臉美感的仿生全局區(qū)域,并驗證了仿生全局區(qū)域的有效性,最后從仿生全局區(qū)域中提取圖像的中層語義特征。該方法的目的是得到圖像中層語義的特征表示,相較于只包含圖像基本信息的圖像底層特征表示的方法,本方法因其對圖像語義的更好理解使其具有更好的表達能力,在分類能力上也具有更好的效果。實驗結(jié)果表明這種基于圖像中層語義的特征提取方法在人臉美感表達中表現(xiàn)出更好的效果。(二)提出了一種基于TrueSkill算法的人臉美感標簽確定方法。直接通過被試對樣本美感進行打分不夠精確,得到樣本之間美感的相對排序卻比較簡單,本文首先建立由相同規(guī)格人臉圖像組成的樣本集,通過美感排序?qū)嶒灥玫奖辉噷γ拷M樣本圖像的美感排序結(jié)果,然后通過TrueSkill算法將相對美感排序轉(zhuǎn)換為絕對的美感分數(shù),最后通過加權(quán)平均得到人臉美感標簽。(三)提出了一種基于深度學習和模型融合的人臉美感分析框架。本框架首先通過卷積神經(jīng)網(wǎng)絡(luò)訓練出一系列基于仿生全局特征的仿生屬性檢測器,并得到訓練樣本的概率輸出,然后將每個訓練樣本在不同檢測器中的概率輸出融合為該樣本的特征向量,最后結(jié)合美感標簽訓練出人臉美感預測模型。本框架得到的人臉美感預測模型提高了分類算法的準確度。實驗結(jié)果表明,本框架基于深度學習和模型融合的方法不僅相比于單個仿生屬性檢測器具有更高的分類準確度,相比于其它的特征提取方法也表現(xiàn)出了不俗的效果。
[Abstract]:Automatic face aesthetic analysis is a technique that simulates the analysis and evaluation mechanism of human face aesthetic perception by computer, and enables the computer to "learn" the human aesthetic method. In recent years, with the increasing demand of face aesthetic evaluation in different industries, such as beauty, matchmaking, recruitment, multimedia and so on, automatic face aesthetic analysis technology has been developed rapidly. The traditional automatic face aesthetic analysis method is devoted to finding the features and high precision classification algorithms that can affect the aesthetic degree of the face in order to complete the classification and evaluation of different faces. However, these methods can not explain clearly how the human visual system and brain screen and use these features to judge the aesthetic perception of human face in the process of analyzing the effective expression of image information, and lack of scientific methods to statistics and verify its rationality. In order to restore the process of human aesthetic judgment as much as possible and get better classification effect, this paper proposes a face aesthetic prediction method which combines biological heuristics and depth attribute learning. On the one hand, the method based on middle level feature representation is used to improve the performance of image feature extraction algorithm; on the other hand, the method of depth learning and model fusion is used to improve the accuracy of classification algorithm. Finally, a face aesthetic analysis framework based on depth learning and model fusion is constructed. The main work of this paper is as follows: 1. A feature extraction method based on image middle level semantics is proposed. In this method, the salience region of human face and the weight matrix of face salience are extracted by eye movement experiment, and then the bionic global region which determines the aesthetic sense of face is obtained by the "global local" visual attention mechanism. The validity of the bionic global region is verified. Finally, the middle semantic features of the image are extracted from the bionic global region. The purpose of this method is to get the feature representation of image middle level semantics. Compared with the method of image bottom feature representation which contains only the basic information of image, this method has better expression ability because of its better understanding of image semantics. In the classification ability also has the better effect. Experimental results show that the feature extraction method based on image meso-semantic is more effective in facial aesthetic expression. (2) A face aesthetic label determination method based on TrueSkill algorithm is proposed. It is not accurate enough to score the aesthetic feeling of the sample directly, but it is relatively simple to get the relative ranking of the aesthetic feeling between the samples. In this paper, a sample set composed of face images of the same size is first established. The aesthetic ranking results of each sample image were obtained by aesthetic sorting experiment, then the relative aesthetic sorting was transformed into absolute aesthetic score by TrueSkill algorithm, and the face aesthetic label was obtained by weighted average. (3) A face aesthetic analysis framework based on deep learning and model fusion is proposed. Firstly, a series of bionic attribute detectors based on bionic global features are trained by convolution neural network, and the probabilistic output of training samples is obtained. Then the probabilistic output of each training sample in different detectors is fused into the feature vector of the sample. Finally, a face aesthetic prediction model is trained by combining the aesthetic label. The prediction model of facial aesthetic perception obtained by this framework improves the accuracy of the classification algorithm. The experimental results show that the method based on depth learning and model fusion not only has higher classification accuracy than the single bionic attribute detector, but also has a good effect compared with other feature extraction methods.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

【相似文獻】

相關(guān)期刊論文 前1條

1 高超;王坤茜;;計算機人機界面交互的美感體現(xiàn)[J];價值工程;2014年17期

相關(guān)碩士學位論文 前1條

1 李璐;融合生物啟發(fā)與深度屬性學習的人臉美感預測方法[D];鄭州大學;2017年

，

本文編號：1808827