【摘要】：隨著大數(shù)據(jù)時代和智能化的到來,如何高效的從人臉圖像數(shù)據(jù)中挖掘出有價值的信息,已經(jīng)成為人臉識別領域研究的熱點。隨著云計算、分布式批處理的飛速發(fā)展,給人臉識別帶來了新的思路。傳統(tǒng)人臉識別技術只針對小范圍內(nèi)、靜止狀態(tài)下、單個人臉識別的研究。通常在數(shù)據(jù)量增大的時候實時性非常低,因此無法使用在數(shù)據(jù)量較大的場所。本文對人臉識別在適用范圍和實時性進行探索,提出了將現(xiàn)有的人臉識別算法和大數(shù)據(jù)處理架構中的批處理計算框架MapReduce相結合的新思路。本文的主要貢獻如下:1)針對傳統(tǒng)人臉識別適用范圍小的問題,提出了使用大數(shù)據(jù)存儲技術Hadoop中的HDFS和HBase來存儲數(shù)據(jù)。將所有的內(nèi)置圖片和待識別的人臉圖片存放在HBase中,將唯一代表內(nèi)置圖片信息的文本文件存放在HDFS中,進而使得人臉識別可以應用于范圍更大的場所。2)針對傳統(tǒng)人臉識別實時性低的問題,提出了將人臉識別算法和Hadoop中的批處理MapReduce相結合的思路。首先對人臉識別PCA算法,使用Map計算歐式距離,得到處理的中間的結果。然后用Reduce處理此中間結果,最后將得到的最小歐式距離所對應的內(nèi)置圖片信息作為最終結果并存儲。為測試本文改進的人臉識別系統(tǒng)的性能,用多組不同數(shù)量的內(nèi)置人臉圖片數(shù)據(jù)來評估對人臉識別模型改進的實時性和適用范圍,得到了良好的測試結果。
[Abstract]:With the era of big data and the arrival of intelligence, how to efficiently extract valuable information from face image data has become a hot spot in the field of face recognition. With the rapid development of cloud computing, distributed batch processing has brought new ideas to face recognition. The traditional face recognition technology only focuses on single face recognition in a small range and static state. Generally, the real-time performance is very low when the amount of data increases, so it can not be used in places with large amount of data. This paper explores the applicability and real-time of face recognition, and proposes a new idea of combining the existing face recognition algorithm with the batch computing framework (MapReduce) in big data's processing architecture. The main contributions of this paper are as follows: (1) aiming at the problem of small application range of traditional face recognition, this paper proposes to use HDFS and HBase in big data storage technology to store data. All the built-in images and face images to be recognized are stored in the HBase, and the unique text files representing the built-in picture information are stored in the HDFS. So that face recognition can be applied to a larger range of places. 2) aiming at the problem of low real-time performance of traditional face recognition, the idea of combining face recognition algorithm with batch processing MapReduce in Hadoop is proposed. Firstly, the Euclidean distance is calculated by using Map to calculate the Euclidean distance for face recognition PCA algorithm, and the intermediate results are obtained. Then the intermediate result is processed by Reduce, and the built-in picture information corresponding to the minimum Euclidean distance is finally stored as the final result. In order to test the performance of the improved face recognition system in this paper, the real-time and applicable range of the improved face recognition model is evaluated by using a number of different sets of built-in face image data, and a good test result is obtained.
【學位授予單位】：西安科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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