加權信息熵與增強局部二值模式結合的人臉識別

丁蓮靜 劉光帥 李旭瑞 陳曉文

摘 要：針對人臉識別因光照、姿態(tài)、表情、遮擋及噪聲等多種因素的影響而導致的識別率不高的問題，提出一種加權信息熵（IEw）與自適應閾值環(huán)形局部二值模式（ATRLBP）算子相結合的人臉識別方法（IE（w）ATR-LBP）。首先，從原始人臉圖像分塊提取信息熵，得到每個子塊的IEw;然后，利用ATRLBP算子分別對每個人臉子塊提取特征從而得到概率直方圖;最后，將各個塊的IEw與概率直方圖相乘，再串聯(lián)成為原始人臉圖像最后的特征直方圖，并利用支持向量機（SVM）對人臉進行識別。在AR人臉庫的表情、光照、遮擋A和遮擋B四個數(shù)據(jù)集上，IE（w）ATR-LBP方法分別取得了98.37%、94.17%、98.20%和99.34%的識別率。在ORL人臉庫上，IE（w）ATR-LBP方法的最大識別率為99.85%;而且在ORL人臉庫5次不同訓練樣本的實驗中，與無噪聲時相比，加入高斯和椒鹽噪聲后的平均識別率分別下降了14.04和2.95個百分點。實驗結果表明，IE（w）ATR-LBP方法能夠有效提高人臉在受光照、姿態(tài)、遮擋等影響時的識別率，尤其是存在表情變化及脈沖類噪聲干擾時的識別率。

關鍵詞：人臉識別;局部二值模式;加權信息熵;自適應閾值;深度學習

中圖分類號：?TP391.4

文獻標志碼：A

Face recognition combining weighted information entropy with enhanced local binary pattern

DING Lianjing， LIU Guangshuai*， LI Xurui， CHEN Xiaowen

School of Mechanical Engineering， Southwest Jiaotong University， Chengdu Sichuan 610031， China

Abstract：?Under the influence of illumination， pose， expression， occlusion and noise， the recognition rate of faces is excessively low， therefore a method combining weighted Information Entropy （IEw） with Adaptive-Threshold Ring Local Binary Pattern （ATRLBP） （IEwATR-LBP） was proposed. Firstly， the information entropy was extracted from the sub-blocks of the original face image， and then the IEw of each sub-block was obtained. Secondly， the probability histogram was obtained by using ATRLBP operator to extract the features of face sub-blocks. Finally， the final feature histogram of original face image was obtained by concatenating the multiplications of each IEw with the probability histogram， and the recognition result was calculated through Support Vector Machine （SVM）. In the comparison experiments on the illumination， pose， expression and occlusion datasets from AR face database， the proposed method achieved recognition rates of 98.37%， 94.17%， 98.20%， and 99.34% respectively; meanwile， it also achieved the maximum recognition rate of 99.85% on ORL face database. And the average recognition rates in 5 experiments with different training samples were compared to conclude that the recognition rate of samples with Gauss noise was 14.04 percentage points lower than that of samples without noise， while the recognition rate of samples with salt & pepper noise was only 2.95 percentage points lower than that of samples without noise. Experimental results show that the proposed method can effectively improve the recognition rate of faces under the influence of illumination， pose， occlusion， expression and impulse noise.

本文提出了一種結合加權信息熵與自適應閾值環(huán)形局部二值模式的人臉識別方法，利用信息熵對被分塊的人臉圖像信息加權，以確定提取特征在識別中的比重，提出的環(huán)形局部模式算子在原始LBP基礎上，進一步優(yōu)化了局部特征的提取方式，再加上自適應閾值的融合，在增加抗干擾能力的同時，最后的人臉識別率有了顯著提高。……