我正准备向 IET 期刊发送一篇文章，我对两列表格有些问题，下图展示了我想要的表格。
第一个问题，我的代码生成下表：
由于字体形状存在问题，下图显示了 IET 表的形状：
第二个问题是，当我将两列表格放在带有文本的页面中间时，我得到了以下信息：
有时我会得到以下信息：
梅威瑟：
\documentclass{cta-author}
\usepackage{amsmath}
\usepackage{graphicx}
\usepackage{booktabs,tabularx}
\newtheorem{theorem}{Theorem}{}
\newtheorem{corollary}{Corollary}{}
\newtheorem{remark}{Remark}{}
\begin{document}
\begin{table}[!h]
%\fwprocesstable{Comparison of proposed face-iris multimodal biometric method with some recent and similar state-of-the-art methods \label{tab2}}
\begin{tabular*}{\textwidth}{|p{2.24cm}|p{2.74cm}|p{1.74cm}|p{1.74cm}|p{4.74cm}|p{2.74cm}|}
\toprule
Authors & Feature extraction & Feature reduction & Fusion level & Database and number of image considered & Evaluation results (best EER) \\
\midrule
A. Rattani and M. Tistarelli \cite{9} 2005 & SIFT for face and iris & Spacial sampling & Feature level fusion & CASIA V3 for iris, Equinox for face, 57 subjects with 10 samples for each subject & EER $\backsimeq$ 0.05 \% \\
\midrule
Y. Wang et al. \cite{16} 2003 2 & Face: PCA and FDA for Iris: 2D Gabor for Iris & $/$ & Score level fusion & NLPR for iris good quality. ORL, MIT and Yale for face. 90 subjects with 5 face and 5 iris images for each subject & EER = 0 \% \\
\midrule
M. Eskandari And Toygar \cite{3} 2013 & Face: LBP. Iris: 1D Log-Gabor. & PSO & Score level fusion & (FERET + UBIRIS): 170 subjects with 4 samples for each subject. (BANCA + UBIRIS): 40 subjects with 8 samples for each subject. & (FERET + UBIRIS) provided EER = 2.5 \% (BANCA + UBIRIS) provided EER = 0.5 \%.\\
\midrule
Eskandari, Sharifi \cite{20} 2015 & Iris: 1D log-Gabor. Face: five local and global features. & PSO, BSA & Feature level fusion and score level fusion. & CASIA-Iris-Distance database.
90 subjects with 10 samples for each subject. 5 samples for training and 5 samples for test. &
PSO achieved GAR = 94.44\% at FAR = 0.01\% and EER = 3.78\%.\\
\midrule
KHiari-Hili et al. \cite{21} 2016 & Face: DoG combined with LBPU2. Iris: Masek’s & / & Score level fusion & IV multimodal database. 315 subjects in which 52 subjects used for training and the remainder used for test. & EER equal to 0.63\%, 0.96\% in quality variation and multi-session respectively.\\
\midrule
D. Miao et al \cite{22} 2016 & Face: Gabor filter and LBP. Iris: ordinary filters (OMs). & AdaBoost algorithms & Score level fusion. & CASIA Iris Distance database. 142 subjects. 928 images from the first 50 subjects are used for training and the rest for test. & EER of 0.39\%\\
\midrule
O. Sharifi and M. Eskandari \cite{23} 2016 & 1D Log-Gabor for face and iris & BSA & Hybrid
fusion & CASIA Iris Distance database. 90 subjects with 10 samples for each subject. 5 samples for training and 5 samples for test. & EER = 0.27\% $\pm$ 0.41 Achieved FAR = 0.01\% at FRR = 98.93\% $\pm$ 1.11.\\
\midrule
M. Eskandari and O. Sharifi \cite{24} 2017 & Face: 1D log-Gabor and HD-LBP. Iris: 1D log-Gabor. & BSA and LDA. & Hybrid fusion. & CASIA Iris Distance database. 90 subjects with 10 samples for each subject. 5 samples for training and 5 samples for test.& FRR = 93.91\% at
FAR = 0.01\%\\
\midrule
Proposed method & Multi-resolution 2D Log-Gabor for face and iris. & SRKDA & Hybrid fusion. & CASIA Iris Distance database. 90 subjects with 10 samples for each subject. 5 samples for training and 5 samples for test. & EER = 0.24\% with FAR = 0.06\% and FRR = 99.5\%.\\
\bottomrule
\end{tabular*}
\end{table}
\end{document}