本文選題：大五人格特質(zhì) + 人臉特征點(diǎn)定位�。� 參考：《江西師范大學(xué)》2017年碩士論文

【摘要】：隨著人工智能的高速發(fā)展,加速了全球經(jīng)濟(jì)一體化進(jìn)程,競(jìng)爭(zhēng)已經(jīng)跨越國(guó)界,并對(duì)我國(guó)的經(jīng)濟(jì)和社會(huì)產(chǎn)生了巨大的影響。全球競(jìng)爭(zhēng)的實(shí)質(zhì)是人力資源的競(jìng)爭(zhēng),因此,社會(huì)對(duì)高素質(zhì)人才的需求與日俱增。高校作為各類(lèi)人才的重要輸出地,所承載的使命也顯得越來(lái)越重要。如何在面對(duì)種種壓力和矛盾的背景下,培養(yǎng)一支高素質(zhì)的就業(yè)隊(duì)伍亦成為高校亟待解決的問(wèn)題之一�；谏疃葘W(xué)習(xí)的大五人格特質(zhì)與人臉特征分析旨在運(yùn)用心理學(xué)和人力資源管理等專(zhuān)業(yè)背景,采用人格測(cè)驗(yàn)的方法,結(jié)合人臉識(shí)別技術(shù)和機(jī)器學(xué)習(xí)方法,來(lái)挖掘大五人格特質(zhì)與人臉特征之間存在的關(guān)系,作為教育管理者初步判別學(xué)生大五特質(zhì)的輔助工具,并依據(jù)大五人格理論對(duì)學(xué)生進(jìn)行人格特質(zhì)分析提供進(jìn)一步的參考。為解決上述問(wèn)題,實(shí)現(xiàn)研究目標(biāo),主要工作如下:依據(jù)心理學(xué)人格測(cè)試?yán)碚?認(rèn)為人的人格特質(zhì)與人臉的某些特征存在潛在關(guān)聯(lián),并選用大五人格量表,采用統(tǒng)計(jì)分析法對(duì)大五人格特質(zhì)進(jìn)行測(cè)驗(yàn),進(jìn)而提出了一種基于人臉特征與深度學(xué)習(xí)的大五人格特質(zhì)分析研究。大五人格量表能夠得到相對(duì)真實(shí)可靠的人格特質(zhì),為實(shí)驗(yàn)的準(zhǔn)確性奠定了基礎(chǔ)。為了將被測(cè)試者的人格特質(zhì)與臉部特征關(guān)聯(lián)起來(lái),需要對(duì)每個(gè)被測(cè)試者的頭像進(jìn)行人臉特征提取。利用主動(dòng)形狀模型(ASM)可忽略輸入圖像尺寸大小的優(yōu)勢(shì),故采用改進(jìn)后的ASM進(jìn)行人臉特征點(diǎn)的提取,并選取特征之間的距離占整個(gè)臉部長(zhǎng)或?qū)挼谋壤�、五官特征的長(zhǎng)寬比等32個(gè)具有代表性的特征數(shù)據(jù),為下一步深度學(xué)習(xí)提供訓(xùn)練依據(jù)。采用深度學(xué)習(xí)的方法,通過(guò)深度置信網(wǎng)絡(luò),對(duì)學(xué)生的大五人格特質(zhì)與人臉面部特征進(jìn)行訓(xùn)練和分類(lèi),從而找到大五人格特質(zhì)與人臉特征之間的關(guān)系。利用深度學(xué)習(xí)無(wú)監(jiān)督學(xué)習(xí)分類(lèi)的特性,提出基于人臉特征的深度學(xué)習(xí)方法,并通過(guò)訓(xùn)練樣本對(duì)其相關(guān)權(quán)值進(jìn)行訓(xùn)練更新。將實(shí)驗(yàn)結(jié)果與傳統(tǒng)的人格測(cè)驗(yàn)方式得到的結(jié)果進(jìn)行對(duì)比分析,結(jié)果表明,該方法在時(shí)效性和精確度方面都具有更好的效能。
[Abstract]:With the rapid development of artificial intelligence, the process of global economic integration has been accelerated, competition has crossed national boundaries, and has had a great impact on the economy and society of our country. The essence of global competition is the competition of human resources. As an important export place of all kinds of talents, the mission carried by colleges and universities is becoming more and more important. Under the background of various pressures and contradictions, how to cultivate a high-quality employment team has also become one of the problems to be solved urgently in colleges and universities. The analysis of Big five personality traits and facial features based on deep learning aims at applying psychology and human resource management, personality test, face recognition and machine learning. To explore the relationship between Big five personality traits and facial features, as an auxiliary tool for education administrators to preliminarily judge students' Big five traits, and to provide further reference for students' personality trait analysis based on Big five personality theory. In order to solve the above problems and achieve the research goal, the main work is as follows: according to the psychological personality test theory, it is considered that there is a potential correlation between personality traits and some features of human face, and the Big five Personality scale is selected. This paper uses statistical analysis method to test Big five Personality traits, and then puts forward a kind of Big five Personality trait Analysis Research based on face feature and depth learning. The Big five Personality scale can obtain relatively true and reliable personality traits, which lays a foundation for the accuracy of the experiment. In order to correlate the personality traits of the subjects with the facial features, we need to extract the face features from the heads of each subject. The advantage of the size of input image can be ignored by using active shape model (ASM), so the improved ASM is used to extract facial feature points, and the distance between features is chosen to be the proportion of the length or width of the whole face. There are 32 representative feature data such as aspect ratio of features, which provides training basis for further study. By using the method of deep learning and using the deep confidence network, this paper trains and classifies the students' facial features and personality traits of Big five, and finds out the relationship between the personality traits of Big five and facial features. Based on the feature of unsupervised learning classification, a method of depth learning based on facial features is proposed, and its relevant weights are updated by training samples. The experimental results are compared with those obtained by the traditional personality test. The results show that the proposed method is more effective in terms of timeliness and accuracy.
【學(xué)位授予單位】：江西師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：B848

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