IEEE Trans Image Process - Structured sparse error coding for face recognition with occlusion.

Tópicos

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Resumo

Face recognition with occlusion is common in the real world. Inspired by the works of structured sparse representation, we try to explore the structure of the error incurred by occlusion from two aspects: the error morphology and the error distribution. Since human beings recognize the occlusion mainly according to its region shape or profile without knowing accurately what the occlusion is, we argue that the shape of the occlusion is also an important feature. We propose a morphological graph model to describe the morphological structure of the error. Due to the uncertainty of the occlusion, the distribution of the error incurred by occlusion is also uncertain. However, we observe that the unoccluded part and the occluded part of the error measured by the correntropy induced metric follow the exponential distribution, respectively. Incorporating the two aspects of the error structure, we propose the structured sparse error coding for face recognition with occlusion. Our extensive experiments demonstrate that the proposed method is more stable and has higher breakdown point in dealing with the occlusion problems in face recognition as compared to the related state-of-the-art methods, especially for the extreme situation, such as the high level occlusion and the low feature dimension.

Resumo Limpo

face recognit occlus common real world inspir work structur spars represent tri explor structur error incur occlus two aspect error morpholog error distribut sinc human be recogn occlus main accord region shape profil without know accur occlus argu shape occlus also import featur propos morpholog graph model describ morpholog structur error due uncertainti occlus distribut error incur occlus also uncertain howev observ unocclud part occlud part error measur correntropi induc metric follow exponenti distribut respect incorpor two aspect error structur propos structur spars error code face recognit occlus extens experi demonstr propos method stabl higher breakdown point deal occlus problem face recognit compar relat stateoftheart method especi extrem situat high level occlus low featur dimens

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