The optimization of the problems significantly improves the solution of the complex problems. The reduction in the feature dimensionality is enormously salient to reduce the redundant features and improve the system accuracy. In this paper, an amalgamation of different concepts is proposed to optimize the features and improve the system classification. The experiment is performed on the facial expression detection application by proposing the amalgamation of deep neural network models with the variants of the gravitational search algorithm. Facial expressions are the movement of the facial components such as lips, nose, eyes that are considered as the features to classify human emotions into different classes. The initial feature extraction is performed with the local binary pattern. The extracted feature set is optimized with the variants of gravitational search algorithm (GSA) as standard gravitational search algorithm (SGSA), binary gravitational search algorithm (BGSA) and fast discrete gravitational search algorithm (FDGSA). The deep neural network models of deep convolutional neural network (DCNN) and extended deep convolutional neural network (EDCNN) are employed for the classification of emotions from imagery datasets of JAFFE and KDEF. The fixed pose images of both the datasets are acquired and comparison based on average recognition accuracy is performed. The comparative analysis of the mentioned techniques and state-of-the-art techniques illustrates the superior recognition accuracy of the FDGSA with the EDCNN technique.

中文翻译：

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基于深度神经网络的分类优化，通过重力搜索算法的变体降低特征维数

问题的优化显着提高了复杂问题的解决方案。特征维数的降低对于减少冗余特征和提高系统精度具有非常重要的意义。在本文中，提出了不同概念的融合，以优化特征并改进系统分类。通过提出将深度神经网络模型与重力搜索算法的变体相融合，在面部表情检测应用上进行了实验。面部表情是面部成分的运动，例如嘴唇、鼻子、眼睛，它们被视为将人类情绪分类为不同类别的特征。使用本地二进制模式执行初始特征提取。提取的特征集通过引力搜索算法（GSA）的变体进行优化，如标准引力搜索算法（SGSA）、二元引力搜索算法（BGSA）和快速离散引力搜索算法（FDGSA）。深度卷积神经网络 (DCNN) 和扩展深度卷积神经网络 (EDCNN) 的深度神经网络模型用于 JAFFE 和 KDEF 图像数据集的情绪分类。获取两个数据集的固定姿势图像，并进行基于平均识别精度的比较。上述技术和最先进技术的比较分析说明了 FDGSA 与 EDCNN 技术的卓越识别精度。二元引力搜索算法（BGSA）和快速离散引力搜索算法（FDGSA）。深度卷积神经网络 (DCNN) 和扩展深度卷积神经网络 (EDCNN) 的深度神经网络模型用于 JAFFE 和 KDEF 图像数据集的情绪分类。获取两个数据集的固定姿势图像，并进行基于平均识别精度的比较。上述技术和最先进技术的比较分析说明了 FDGSA 与 EDCNN 技术的卓越识别精度。二元引力搜索算法（BGSA）和快速离散引力搜索算法（FDGSA）。深度卷积神经网络 (DCNN) 和扩展深度卷积神经网络 (EDCNN) 的深度神经网络模型用于 JAFFE 和 KDEF 图像数据集的情绪分类。获取两个数据集的固定姿势图像，并进行基于平均识别精度的比较。上述技术和最先进技术的比较分析说明了 FDGSA 与 EDCNN 技术的卓越识别精度。深度卷积神经网络 (DCNN) 和扩展深度卷积神经网络 (EDCNN) 的深度神经网络模型用于 JAFFE 和 KDEF 图像数据集的情绪分类。获取两个数据集的固定姿势图像，并进行基于平均识别精度的比较。上述技术和最先进技术的比较分析说明了 FDGSA 与 EDCNN 技术的卓越识别精度。深度卷积神经网络 (DCNN) 和扩展深度卷积神经网络 (EDCNN) 的深度神经网络模型用于 JAFFE 和 KDEF 图像数据集的情绪分类。获取两个数据集的固定姿势图像，并进行基于平均识别精度的比较。上述技术和最先进技术的比较分析说明了 FDGSA 与 EDCNN 技术的卓越识别精度。