【摘要】：近幾年出現(xiàn)的壓縮感知理論突破了傳統(tǒng)采樣對(duì)于采樣率的限制,為信號(hào)采集和信號(hào)處理提供了新的思路。壓縮感知引起了很多學(xué)者的關(guān)注,根據(jù)壓縮感知理論,有學(xué)者提出了一種新的人臉識(shí)別算法——稀疏表示分類器(SRC)。SRC算法的創(chuàng)新點(diǎn)就在于其直接利用訓(xùn)練集中的人臉圖像構(gòu)成基矩陣,而后將測試集中的人臉圖片用基矩陣線性表示,求出最稀疏解,最后就可以從最稀疏解中提取出分類信息。但是如果把SRC算法直接應(yīng)用到人臉圖像上,算法的計(jì)算復(fù)雜度太高。在本文中我們提出了一種改進(jìn)算法,就是把SRC算法和二維特征提取算法相結(jié)合。在利用SRC算法進(jìn)行人臉識(shí)別之前,我們先用二維特征提取算法對(duì)原始圖片進(jìn)行特征提取和降維。這樣不僅可以降低算法的計(jì)算復(fù)雜度,還可以保留圖像的結(jié)構(gòu)特征。本文的主要工作包括:首先比較了常見的壓縮感知重構(gòu)算法,并且選擇了 OMP算法作為重構(gòu)算法;其次改進(jìn)現(xiàn)有的SRC算法,加入二維特征提取算法來降低算法的計(jì)算復(fù)雜度;最后在AR人臉數(shù)據(jù)庫上進(jìn)行實(shí)驗(yàn)仿真,來驗(yàn)證改進(jìn)算法的可行性和優(yōu)越性。通過對(duì)實(shí)驗(yàn)結(jié)果的分析對(duì)比,證明我們提出的改進(jìn)算法是可行的,并且在識(shí)別率和識(shí)別花費(fèi)的時(shí)間上是優(yōu)于現(xiàn)有算法的。
[Abstract]:In recent years, the theory of compression sensing has broken through the limitation of traditional sampling rate and provided a new idea for signal acquisition and signal processing. Compression perception has attracted the attention of many scholars. According to the theory of compressed perception, Some scholars have proposed a new face recognition algorithm, the sparse representation classifier (SRC) SRC), whose innovation lies in its direct use of the face images in the training set to form the base matrix, and then the face images in the test set are expressed linearly by the basis matrix. Finally, the classification information can be extracted from the sparse solution. However, if the SRC algorithm is directly applied to face images, the computational complexity of the algorithm is too high. In this paper, we propose an improved algorithm, which combines SRC algorithm with two-dimensional feature extraction algorithm. Before using the SRC algorithm for face recognition, we use the two-dimensional feature extraction algorithm to extract and reduce the dimension of the original image. This not only reduces the computational complexity of the algorithm, but also preserves the structural features of the image. The main work of this paper is as follows: firstly, the common compression perception reconstruction algorithms are compared, and the OMP algorithm is selected as the reconstruction algorithm; secondly, the existing SRC algorithm is improved, and two-dimensional feature extraction algorithm is added to reduce the computational complexity of the algorithm. Finally, the experiment is carried out on AR face database to verify the feasibility and superiority of the improved algorithm. Through the analysis and comparison of the experimental results, it is proved that the proposed improved algorithm is feasible, and is superior to the existing algorithm in recognition rate and the time spent in recognition.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

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