ENETCOM, Universite de Sfax, Tunisia.
Kirkuk University, Kirkuk, Iraq.
Biomed Res Int. 2021 Mar 10;2021:6621540. doi: 10.1155/2021/6621540. eCollection 2021.
In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.
在这项研究中，提出了一种基于深度学习的 Gabor 小波变换方法，用于对称人脸数据库。开发了一个建议的人脸识别系统，用于不同的目的。我们使用 Gabor 小波变换对对称人脸训练数据进行特征提取，然后使用深度学习方法进行识别。我们在 ORL 和 YALE 数据库上使用 MATLAB 2020a 实现并评估了所提出的方法。此外，还应用粒子群优化（PSO）进行特征选择方法的相同实验。在我们的研究中，实施具有大量训练图像样本的 Gabor 小波特征提取证明比其他方法更有效。在 ORL 数据库上实施 PSO 方法的识别率为 85.42%，而在 YALE 数据库上则为 92%。然而，使用 PSO 算法将 ORL 数据库的准确率提高到 96.22%，将 YALE 数据库的准确率提高到 94.66%。
