Age And Gender Prediction Using Caffe Model And Opencv (International Journal of Innovative Research in Computer Science & Technology, 2022)
Automatic classification of age and gender  has become crucial for a rising number of applications,  especially as social platforms and social media have risen.  However, there is still substantial lack of performance of  present approaches in real-world photos, especially when  compared to the enormous performance jumps reported  lately in the associated facial recognition job. In this research  we show that a considerable gain in performance may be  achieved by the application of convolution neural networks  (CNN). This work is primarily designed to construct an  algorithm that accurately guesses a person's age and  gender.Haar cascade is one of the most often utilised  approaches. In this research we provide a model that can help  Haar Cascade to determine a person's gender. The model  trained the classifier as positive and negative pictures using  diverse photos of men and women. Various face  characteristics are removed. With the help of Haar Cascade,  the classifier determines if the picture input is men or  women. Even with insufficient data, it functions effectively.  A deep education framework created with Caffe is used to do  the age or sex approximation task. Our model is able to detect  multiple faces in single image and predict age and gender of  all faces present in the image.
Keywords: Haar Cascade; Caffe Model; OpenCV; Convolutional Neural Network