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Robust Face Recognition Against Expressions and Partial Occlusions

International Journal of Automation and computing 2016年第4期13卷 319-337页

作者： Fadhlan Kamaru Zaman amir akramin shafie Yasir Mohd Mustafah Faculty of Electrical Engineering Universiti Teknologi MARA Shah Alam 40450 Selangor Malaysia Department of Mechatronics Engineering International Islamic University Malaysia Kuala Lumpur 50728 Malaysia

Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features＇ contribution according to their variations, we propose a feature selection process that describes facial features as local independent component analysis （ICA） features. These local features are acquired using locally lateral subspace （LLS） strategy. Then, through linear discriminant analysis （LDA） we investigate the intraclass and interclass representation of each local ICA feature and express each feature＇s contribution via a weighting process. Using these weights, we define the contribution of each feature at local classifier level. In order to recognize faces under single sample constraint, we implement LLS strategy on locally linear embedding （LLE） along with the proposed feature selection. Additionally, we highlight the efficiency of the implementation of LLS strategy. The overall accuracy achieved by our approach on datasets with different facial expressions and partial occlusions such as AR, JAFFE, FERET and CK% is 90.70%. We present together in this paper survey results on face recognition performance and physiological feature selection performed by human subjects.

关键词： Face recognition facial expressions dimensionality reduction single sample feature selection.

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Smart Objects Identification System for Robotic Surveillance

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International Journal of Automation and computing 2014年第1期11卷 59-71页

作者： amir akramin shafie Azhar Bin Mohd Ibrahim Muhammad Mahbubur Rashid Department of Mechatronics Engineering Faculty of EngineeringInternational Islamic University Malaysia

Video surveillance is an active research topic in computer *** this paper,humans and cars identifcation technique suitable for real time video surveillance systems is *** technique we proposed includes background subtraction,foreground segmentation,shadow removal,feature extraction and *** feature extraction of the extracted foreground objects is done via a new set of afne moment invariants based on statistics method and these were used to identify human or *** the partial occlusion occurs,although features of full body cannot be extracted,our proposed technique extracts the features of head *** proposed technique can identify human by extracting the human head-shoulder up to 60%–70%***,it has a better classifcation to solve the issue of the loss of property arising from human occluded easily in practical *** whole system works at approximately 16 29 fps and thus it is suitable for real-time *** accuracy for our proposed technique in identifying human is very good,which is 98.33%,while for cars identifcation,the accuracy is also good,which is 94.41%.The overall accuracy for our proposed technique in identifying human and car is at 98.04%.The experiment results show that this method is efective and has strong robustness.

关键词： Humans and cars identifcation partially occluded human afne moment invariants video surveillance systems machine vision

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