Occluded Face Recognition Using Machine Learning

Abstract

A fascinating and intense issue nowadays is the difficult problem of identifying human faces when they are obscured by various obstructions. Due to a sizable occluded zone and a dearth of un-occluded portions, the capacity of most identification algorithms goes off dramatically. We describe a pre-processing approach for occluded faces that is effective and features-based to the fullest extent. Pose, lighting, age, emotions, and hair covering the face are among the 40 different occlusions that are included in the dataset. Edge-preserving enhancement and contrast measurement-correction for the occluded face pictures are part of the two simultaneous procedures used in the pre-processing step. Utilizing Gabor coefficients, Linear Binary Patterns based on Haar Wavelet components, and Histogram of Gaussian features, the finest textural characteristics are retrieved. The other set of characteristics used to represent the entire occluded face region is composed of wavelet components, color histograms, and statistical global features based on first order. In order to validate utilizing support vector machines, the study takes into account 100 celebrities from the CelebA dataset with posture and other occlusions. Compared to other well- known systems, the suggested efficient pre-processing and optimal features-based face recognition system exhibited increased classification accuracy in experiment analysis.