Support Vector Machine Based Feature Extraction for Gender Recognition From Objects Using Lasso Classifier
Abstract The blind people has their difficulty to identify the object moving around them, therefore with a high accuracy score object detection and human face recognition system will helps them in identifying the things around them with ease. In this research article,a minimum distance trainer for feature selection by accessing SVM feature optimization process, then LASSO classifier applied to perform object recognition and gender classification. Database of 100 images (50 male and 50 female face images considered from 5 different databases) and 10 categories of vehicle types are used for gender and vehicle recognition and classification. Original face image database used for the gender classification. This approach was implemented with dual classification model [(1) Recognizing or classifying 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), lasso regression(LR) and lasso regression with Gaussian Support Vector Machines (LRGS) based classificatioins. The final classification results accurate are as follows RR- 89.6%, EN- 93.5%, LR-93.2% and the proposed approach is LRGS with 98.4% accurate detection rate with rediction names.