Age and Gender Detection System using Raspberry Pi - Biradar, Sumangala; Torgal, Beena; Hosamani, Namrata; Bidarakundi, Renuka; Mudhol, Shruti (International Journal of Computer Sciences and Engineering, 2019)
Since the rise in social media and interactive systems in recent decades, the automatic classification of age and gender has become relevant to most of the social platforms and human-computer interactions. To achieve this task, many methods are implemented, but somehow those are not effective with real-world images as most of the models are trained using images of limited dataset taken from lab settings which are generally constrained in nature. Such images do not contain variations of appearance which are usually observed in images of the real world such as social networks, online repositories, and websites. In this paper, we try to improve the performance by making use of the deep convolutional neural network (CNN). The proposed network architecture uses Adience benchmark for gender as well as age estimation and its performance are much better with real-world images of the face. Here in this paper, a suitable method is described for the detection of a face at real-time and estimation of their age and gender. It first detects whether a face is there or not in the image captured. If it is present, the face is detected and the region of face content is returned using colored square structure and returns their age and gender as a result. The convenient and easy hardware implementation for this method is by utilizing a Raspberry-Pi kit and camera, as it is a minicomputer of credit card size. To build an effective age and gender estimator, the concept of Deep Convolution Neural Network is used. The input data contains different age groups of male and female face images. Over captured faces’ feature extraction are compared with this input data to evaluate the age as well as the gender of the person.