本文選題：人臉識別　切入點：服務推送　出處：《海南大學》2017年碩士論文　論文類型：學位論文

【摘要】：傳統(tǒng)的Web推送服務大多是根據系統(tǒng)的登錄賬號、歷史記錄或者訂閱記錄的分析判斷進行內容的推送,這樣的推送機制不能充分體現使用者個體間的喜好差異。自從Web2.0技術推出以來,基于該技術的智能化、個性化的推送服務和應用成為該領域的研究熱點。本文根據服務大廳的個性化服務的實際需求,探討將人臉識別技術與服務推送技術相結合,實現基于人臉識別的個性化、智能服務推送系統(tǒng)。在實際的推送服務系統(tǒng)中,一個用戶在樣本庫中通常只有一幅頭像,所以本文將重點探討單樣本的人臉特征提取算法和識別技術。在分析基于主成份分析法的單樣本人臉識別經典算法((PC)2 A、SPCA)優(yōu)缺點的基礎上,提出了通過SPCA算法提取全局特征,通過M(2D)2PCA+PCA算法提取局部特征,然后通過最大隸屬度分別求得基于全局特征和基于局部特征的識別結果,最后采用模糊綜合方法在決策層進行加權融合,并在ORL人臉庫和Yale人臉庫上進行了算法驗證。實驗結果表明,該算法具有良好的識別性能,在ORL人臉庫的識別率為89.94%,在Yale人臉庫的識別率為90.94%;并且對表情、姿態(tài)、光照等的變化具有較好的魯棒性。為了提供更精準的個性化服務推送,對基于用戶個人數據分析的個性化服務技術進行了深入的探討,建立了基于用戶協同過濾算法(UBCF)模型,最后通過PUSHLET技術將服務信息推送給用戶。最后,以服務大廳取號機系統(tǒng)為例,研發(fā)了基于人臉識別的智能推送服務取號原型系統(tǒng),該系統(tǒng)由人臉識別、賬號管理、信息獲取以及服務推送等四部分構成,實現了服務推送系統(tǒng)的個性化、智能化的目的。
[Abstract]:The traditional Web push service is mostly based on the system's login account, history or subscription records of the analysis of the content of the push, This push mechanism does not fully reflect the differences of preferences among users. Since the introduction of Web2.0 technology, based on the intelligence of that technology, Personalized push service and application have become the research hotspot in this field. According to the actual demand of personalized service in service hall, this paper discusses the combination of face recognition technology and service push technology to realize personalization based on face recognition. An intelligent service push system. In a real push service system, a user usually has only one image in the sample library. So this paper will focus on the single sample face feature extraction algorithm and recognition technology. On the basis of analyzing the advantages and disadvantages of single sample face recognition classic algorithm based on principal component analysis, this paper proposes to extract global features by SPCA algorithm. The local feature is extracted by MJ 2DX 2PCA PCA algorithm, then the recognition results based on global feature and local feature are obtained by the maximum membership degree. Finally, the fuzzy synthesis method is used for weighted fusion at the decision level. The algorithm is validated on ORL face database and Yale face database. The experimental results show that the algorithm has good recognition performance, the recognition rate in ORL face database is 89.94, the recognition rate in Yale face database is 90.94, and the recognition rate is 90.94 for expression and pose. In order to provide more accurate personalized service push, the personalized service technology based on user personal data analysis is deeply discussed, and the UBCF-based user collaborative filtering algorithm is established. Finally, the service information is pushed to the user by PUSHLET technology. Finally, a prototype system of intelligent push service number retrieval based on face recognition is developed, which is managed by face recognition and account number. Information acquisition and service push are composed of four parts, which realize the purpose of individuation and intelligence of service push system.
【學位授予單位】：海南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP391.3

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