Age-Invariant Face Recognition Using Trigonometric Central Features

Abstract

Many facial recognition systems must fail because of many influences such as lighting, changes in composition, expression and aging in the face. But the effects of facial aging are the main problem that many algorithms face with precision. Accordingly, this work provides a model based on the extraction of a number of trigonometric features. The proposed model includes three areas connecting and surrounding the main facial features. In addition, aging is recognized by calculating the trigonometric zones extracted. The system compares the query face with the database. Then, the query image is extracted from the original face image. Next, the performance of the proposed model is compared with some of the latest facial recognition techniques. Facial recognition systems in different stages of life prove that the proposed facial recognition system gives enhanced accuracy of 99.80% with very low FAR level of 0.0001.

Propose shot boundary detection methods by using visual hybrid features

“…Features extraction process is an essential step in data analysis tasks used to extract important information from multimedia data [9], [10]. Features are defined as a descriptive parameter.…”