Object detection and gender recognition were two different categories to be classified in a single section is a complicated task and this approach helps in supporting the blind people for an artificial vision. In this paper, our method to the betters vision sensation of blind persons by conversion of visualized data to audio data. Therefore this artificial intelligence model helps in detecting the objects as well as human face recognition with gender classification based on face recognition approach. This model processed with feature extraction and classification models. The feature extraction was comprised with multi scale-invariant feature transform (MSIFT), with feature optimization with support vector machine algorithm then classified using LASSO classifier. For better performance identification, three different classification models were implemented and tested too. Feature selection helps in making tests early to detect the objects and recognizing human actions using image processing approach. This approach can be applied for both offline and online modes. But in this scenario, an offline mode was implemented and was tested with a combination of different databases. For this process of classification ridge regression (RR), elastic net (EN), lasso regression (LR) and LASSO regression were implemented. The final classification results with accuracy are as follows for RR-89.6%, EN-93.5%, LR-93.2% and proposed approach (LRGS) with 98.4% accurate detection rate with prediction name of classes.

对象检测和性别识别是要在一个部分中分类的两个不同类别，这是一项复杂的任务，这种方法有助于为盲人提供人造视觉的支持。在本文中，我们的方法通过将可视化数据转换为音频数据来改善盲人的视觉感受。因此，该人工智能模型有助于基于面部识别方法对物体进行检测，并通过性别分类对人脸进行识别。该模型使用特征提取和分类模型进行处理。使用多尺度不变特征变换（MSIFT）进行特征提取，然后使用支持向量机算法对特征进行优化，然后使用LASSO分类器进行分类。为了更好地识别性能，三种不同的分类模型也已实施和测试。特征选择有助于尽早进行测试以检测物体并使用图像处理方法识别人为动作。这种方法可以应用于离线和在线模式。但是在这种情况下，实现了脱机模式，并结合了不同的数据库对其进行了测试。对于此分类过程，实施了岭回归（RR），弹性网（EN），套索回归（LR）和LASSO回归。最终的准确分类结果如下：RR-89.6％，EN-93.5％，LR-93.2％和提议的方法（LRGS），准确率为98.4％，具有预测类别。这种方法可以应用于离线和在线模式。但是在这种情况下，实现了脱机模式，并结合了不同的数据库对其进行了测试。对于此分类过程，实施了岭回归（RR），弹性网（EN），套索回归（LR）和LASSO回归。最终的准确分类结果如下：RR-89.6％，EN-93.5％，LR-93.2％和提议的方法（LRGS），准确率为98.4％，具有预测类别。这种方法可以应用于离线和在线模式。但是在这种情况下，实现了脱机模式，并结合了不同的数据库对其进行了测试。对于此分类过程，实施了岭回归（RR），弹性网（EN），套索回归（LR）和LASSO回归。最终的准确分类结果如下：RR-89.6％，EN-93.5％，LR-93.2％和提议的方法（LRGS），准确率为98.4％，具有预测类别。