Abstract
Object detection and gender recognition are the two different categories to be classified in a single section is a complicated task and needs to support the blind people. In this paper, our method to the better sensation of blind persons by conversion of visualized data to audio data. Therefore the artificial intelligence model requires to detect the objects as well as human face recognition with gender classification algorithms. 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.