【摘要】：該文第一部分提出的人臉識別系統(tǒng)是為安保服務型機器人提供的。考慮到實際情況的復雜不確定性,該系統(tǒng)的設計針對人臉的角度、大小、環(huán)境與光照等有影響的因素都進行了處理來減小誤差。利用局部二值算子的旋轉(zhuǎn)不變性實現(xiàn)多角度的人臉檢測,然后用SVM算法對檢測出的人臉進行識別,效果比基于Harr算子和某些普通算法的人臉識別系統(tǒng)功能更加完善強大。另外,機器人對于視覺系統(tǒng)的實時性要求比較高。算法如果太過復雜會減慢處理速度,同樣,硬件的參數(shù)對此也有很大的影響。如何在保證精確度與實時性的情況下選取合適的算法與硬件,對于整個安保服務機器人的視覺系統(tǒng)至關重要。本文使用局部二值和支持向量機算法,以及E9卡片機完成了設計,在精確度與實時性上均滿足機器人的需求。第二部分內(nèi)容是基于卷積神經(jīng)網(wǎng)絡的帶遮擋人臉識別,當一張人臉圖像部分尤其是關鍵部分被遮擋之后,識別這個人的身份就變得更加困難。卷積神經(jīng)網(wǎng)絡是現(xiàn)今深度學習中的一種常見算法,目前卷積神經(jīng)網(wǎng)絡在人臉識別中的使用效果也十理想,在實驗過程中,我們發(fā)現(xiàn)卷積神經(jīng)網(wǎng)絡的魯棒性很強大,于是將其運用到帶遮擋的人臉識別上,取得了比很多經(jīng)典人臉識別算法更好的結果
[Abstract]:The face recognition system proposed in the first part of this paper is for the security service robot. Considering the complex uncertainty of the actual situation, the design of the system is aimed at the face angle, the size, the environment and the illumination and other influential factors are processed to reduce the error. Using the rotation invariance of local binary operator to realize multi-angle face detection, and then using SVM algorithm to recognize the detected face, the effect is more perfect than that of the face recognition system based on Harr operator and some common algorithms. In addition, the robot has a high requirement for real-time vision system. If the algorithm is too complex, it will slow down the processing speed. How to select the appropriate algorithm and hardware under the condition of ensuring accuracy and real-time is very important to the vision system of the whole security service robot. In this paper, the local binary and support vector machine algorithms and the E9 card machine are used to complete the design, which meet the requirements of the robot in accuracy and real-time. The second part is based on convolution neural network. When a face image part, especially the key part, is occluded, it becomes more difficult to recognize the identity of the person. Convolution neural network (Ann) is a common algorithm in depth learning nowadays, and the application effect of convolution neural network in face recognition is also ten ideal. In the process of experiment, we find that the robustness of convolution neural network is very strong. Therefore, it is applied to face recognition with occlusion, and better results are obtained than many classical face recognition algorithms.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP242

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相關期刊論文 前1條

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本文編號：2292081