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 better sensation of a 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 recognising human actions using image processing approach. This can be applied for both offline and online modes. But in this scenario offline mode was implemented and was tested with 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 classfication results with accuracy are as follows for RR- 89.6%, EN- 93.5%, LR-93.2% and propsed approach(LRGS) with 98.4% accurate detection rate with prediction name of classes.

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.

No abstract

Image processing is a field in which biometric traits such as Face, voice, lip movements, hand geometry, odour, gait, iris, retina, fingerprint etc., are essential for recognition. The face is the most critical biometric trait for recognition because the face is an easily approachable biometric trait. There is no need for attention from a human being for face recognition. Human face classification is a challenging task for a machine. In this project, minimum distance classifier used with LASSO based gender classification. Database of 100 images (50 male and 50 female face images which considered from 4 different databases) used for face recognition and classification. Original face image database used for the gender classification. This approach of dual classfication ((1) Recognizing or classfying human faces from various objects and (2) Classifying gender through face recognition) is made possible with the help of combining modified SIFT feature in combination with ridge regression (RR), elastic net (EN) and lasso regression with GSVM (LRGS) based classificatioins. The final classfication results with accuracy are as follows for RR- 89.6%, EN- 93.5%, LR-93.2% and propsed approach(LRGS) with 98.4% accurate detection rate with rediction names.

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.