Introduction
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.
Methodology
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.