Age detection using Neural network in MATLAB

MATLABSolutions demonstrate how to use the MATLAB software for simulation of This Repository is designed to detect the age from an image using Neural network features. The Neural network is used as a classifier for it. FGNET database is used which has 1002 sample images of different objects at different ages. The number of samples in each class is highly non -uniform. So the data is oversampled first and then trained.

The neural network in machine learning facilitates automation in image processing facilities which is either number plate recognition, face detection, expression recognition, joining images at different exposures etc. It also eases the task to recognize and verify the ages from the images which help in passports and other related applications. This MATLAB code for age detection from images contributes towards that fact. We developed this code at free-thesis. We used FGNET aging dataset for the features generation and testing. Though testing also worked on other images that don’t have covered faces by sunglasses or by any other means. The complete pipeline is like this:

Abstract

The problem of determining people’s age is a recurring theme in areas such as law enforcement, education and sports because age is often used to determine eligibility. The aim of current work is to make use of a lightweight machine learning model for automating the task of detecting people’s age. This paper presents a solution that makes use of a lightweight Convolutional Neural Network model, built according to a modification of the LeNet-5 architecture to perform age detection, for both males and females. The UTK-Face Large Scale Face Dataset was used to train and test the performance of the model in terms of predicting age. To evaluate the model’s performance in real-time, Haar Cascades were used to detect faces from video feeds. The detected faces were fed to the model for it to make age predictions. Experimental results showed that age-detection can be performed in real-time. Although, the prediction accuracy of the model requires improvement.

Introduction

Biometrics refers to the automatic recognition (verification and identification) of individuals based on their physical appearance, behavioral traits, and/or their compound effects. Common biometric modalities include face, fingerprints, iris, voice, signature, and hand geometry. Face authentication for recognition purposes in uncontrolled settings is challenged by the variability found in biometric footprints. Variability is due to intrinsic factors such as aging, or extrinsic factors such as image quality, pose, or occlusion. The performance of a biometric system further depends on demographics, image representation, and soft biometrics. This paper is concerned with face recognition subject to aging.

Biometrics is widely used in forensics and security applications such as access control and surveillance. The face biometric traits are usually extracted using a camera sensor and are represented as templates. A database known as the gallery stores the templates for all the known subjects. Given an unknown subject (probe), a biometric system can be used for either verification or identification. In verification mode, a probe template is compared to a single template from the gallery to determine if the two templates belong to the same subject or not. In identification mode, the probe template is compared to all the templates in the gallery to determine the closest match. Identification can be viewed as multiple verifications.