Intelligent Age Estimation Using Facial Feature Analysis and Machine Learning Techniques
Keywords:
Age Estimation, Facial Feature Extraction, Machine Learning, Pattern Recognition, Support Vector Machine, Viola-Jones Algorithm, Wrinkle Area ClassifierAbstract
Facial Age Estimation has lately become an important research field in biometric recognition, security and human computer-interaction applications. The right age classification enables various applications, such as accessing identity documents and delivering tailored services. The aim of this paper is to develop an intelligent age estimation system, using a combination of facial feature extraction, wrinkle area classification and a Support Vector Machine (SVM) classifier, to boost the accuracy of the age estimation. The system is able to capture the face wrinkles position, eyes position and structural changes over the face which are used for the age indicators. The system utilizes the Viola-Jones algorithm which is one of the common algorithms for real-time object detection, to separate regions of interest, e.g., the area containing wrinkles, and to determine the area that is relevant to the age estimation problem. Additionally, the Euclidean distance calculation is used to estimate the spatial relation between the facial features, further reinforcing the representation of features. The system was trained and tested on 150 facial images categorized into 6 age classes with a gap of 10 years. On this dataset, classification accuracy of 98.89 percent was attained by the proposed method which is better than some other age estimation methods that were tested on similar benchmark data. The system is capable of that kind of accuracy and it could be used in real-world applications in the fields of biometric security, forensic science, and age-based user profiling. This work not only helps in the identity verification and personalized user experience but also adds to the existing body of research on facial recognition systems in security and consumer technology applications.
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