Facial Skin Texture and Distributed Dynamic Kernel Support Vector Machine (DDKSVM) Classifier for Age Estimation in Facial Wrinkles

Abstract

Facial wrinkles are common aspects of ageing human skin that may be used in a variety of image-based ageing applications. Wrinkles on the face are 3D skin characteristics that appear as tiny discontinuities or fissures, as well as inconsistencies in the surrounding skin texture. Existing image-based techniques to ageing skin analysis focus on wrinkles as texture rather than curvilinear discontinuity/crack or irregularity characteristics. Picture processing methods are useful for reconstructing and manipulating an image in order to produce various images. The goal of this study is to create a FWDAE-IP (Face Wrinkles Detection and Age Estimate with Image Processing) system that can identify the exact position of wrinkle lines in relation to cracks/ discontinuities, abnormalities, and age estimation using skin texture data from one face image. This FWDAE-IP system is performed based on four major steps: Preprocessing, Skin texture feature extraction, wrinkles detection, and age estimations. In the first step, preprocessing is performed by rotation angle. In the second step, GFs (Gabor Filters), ALTPs (Adaptive Local Ternary Patterns), and AAMs (Active Appearance Models) are introduced against variation in pose or illumination. In the third step, GMM-EM (Gaussian Mixture Model- Expectation-Maximization) is introduced for detecting irregularities in the wrinkles, Gabor filters and image morphology is introduced for detecting discontinuity/crack in the wrinkles. In the fourth step, age estimation is performed based on the detected with wrinkles by DDKSVM (Distributed Dynamic Kernel Support Vector Machine) classifier. The results of age estimation methods are measured via the evaluation metrics such as MAEs (Mean Absolute Error), CSs (Cumulative Scores), accuracy, SSIMs (structural Similarity Indices), and JSIs (Jcard Similarity Indices). The experimentation work is performed based on FG-NET database.