Identification of Gender from Facial Features

Abstract

Increasing population and changing lifestyle become a more confusing task for detecting gender from facial images. To solve such a fragile problem several handy approaches are readily available in computer vision. Although, very few of these approaches achieve good accuracy. The features like lightning, illumination, noise, ethnicity, and various facial expression hamper the correctness of the images. Keeping these things in mind, we propose our research work on the identification of gender from facial features. The major component of face recognition is to develop a machine learning model which will classify the images this can be done by haar-cascade-classifier. To train the model with images more accurately we would perform few image processing concepts for the data to perform data analysis and preprocessing for structuring our data. This can be done by OpenCV. After that, we have used PCA ( Principle Comprehend Analysis ) to compute Eigenvalues and for the optimal components, we will get the class name from the knowledge base and confidence score from the SVM-based face recognition model. In our project work, we get good accuracy.