Abstract
Problem statement: Face occlusion is a very challenging problem in fac e recognition. The performance of face recognition system decreases dr astically due to the presence of partial occlusion on the face. Extracting discriminative features to achieve accurate detection versus computational overhead in extracting the features, which affects the classification speed, would be a sustained problem. The objective of this study is to segment the human face into non-occluded and occluded part of the occluded human face image. In General, for f ace detection special facial features are extracted . In the proposed study a simplified algorithm to ext ract the features is developed. Approach: An algorithm which enables the automatic detection of the presence of occlusions on the face would be a useful tool to increase the performances of the sys tem. The face image was preprocessed to enhance the input face images in order to reduce the loss o f classification performance due to changes in faci al appearance. The experiment also balances both illumination and facial expression changes. Results: In this study, a Mean Based Weight Matrix (MBWM) algorithm has been proposed to enhance the performance by 4.25% than the LBP method. Conclusion: The proposed model has been tested on occluded face images with a dataset obtained from t he MIT face database.
Highlights
The most important goal of computer vision today is to achieve visual recognition ability akin to that of human
This study focuses on the occlusion detection on the face covered by medical mask
The LBP, SLBM, Mean Based Weight Matrix (MBWM) features are extracted for all the patches
Summary
The most important goal of computer vision today is to achieve visual recognition ability akin to that of human. The face recognition problem has been researched intensively for the past few decades, due to its great potential in various practical applications such as Human Computer Interface (HCI), intelligent robot, surveillance and so on. The observations and findings about human face recognition system will be a good starting point for automatic face attribute analysis. When face recognition is diversified further, obviously problem of occlusion by other objects or apparels such as sunglasses, carves, mask becomes eminent. Show the outline of the occlusion detection process. This has resulted in the development of successful algorithms and the introduction of commercial products. Using the present technology it is impossible to completely model human recognition system and reach its performance and accuracy
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