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Multi angle optimal pattern-based deep learning for automatic facial expression recognition
Jain Deepak Kumar1,2; Zhang Zhang1,2; Huang Kaiqi1,2; Kaiqi Huang
Source PublicationPattern Recognition Letters
2017
IssueXXPages:1-9
Abstract
Facial Expression Recognition (FER) plays the vital role in the Human Computer Interface (HCI) applications. The illumination and pose variations affect the FER adversely. The projection of complex 3D actions on the image plane and the inaccurate alignment are the major issues in the FER process. This paper presents the novel Multi-Angle Optimal Pattern-based Deep Learning (MAOP-DL) method to rectify the problem from sudden illumination changes, find the proper alignment of a feature set by using multi-angle-based optimal configurations. The proposed method includes the five major processes as Extended Boundary Background Subtraction (EBBS), Multi-Angle Texture Pattern+STM, Densely Extracted SURF+Local Occupancy Pattern (LOP), Priority Particle Cuckoo Search Optimization (PPCSO) and Long Short-Term Memory -Convolutional Neural Network (LSTM-CNN). Initially, the EBBS algorithm subtracts the background and isolates the foreground from the images which overcome the illumination and pose variation. Then, the MATP-STM extracts the texture patterns and DESURF-LOP extracts the relevant key features of the facial points. The PPCSO algorithm selects the relevant features from the MATP-STM feature set to speed up the classification. The employment of LSTM-CNN predicts the required label for the facial expressions.The major key findings of the proposed work are clear image analysis, effective handling of pose/illumination variations and the facial alignment. The proposed MAOP-DL validates its effectiveness on two standard databases such as CK+ and MMI regarding various metrics and confirm their assurance of wide applicability in recent applications.
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Facial Expression Recognition (FER) plays the vital role in the Human Computer Interface (HCI) applications. The illumination and pose variations affect the FER adversely. The projection of complex 3D actions on the image plane and the inaccurate alignment are the major issues in the FER process. This paper presents the novel Multi-Angle Optimal Pattern-based Deep Learning (MAOP-DL) method to rectify the problem from sudden illumination changes, find the proper alignment of a feature set by using multi-angle-based optimal configurations. The proposed method includes the five major processes as Extended Boundary Background Subtraction (EBBS), Multi-Angle Texture Pattern+STM, Densely Extracted SURF+Local Occupancy Pattern (LOP), Priority Particle Cuckoo Search Optimization (PPCSO) and Long Short-Term Memory -Convolutional Neural Network (LSTM-CNN). Initially, the EBBS algorithm subtracts the background and isolates the foreground from the images which overcome the illumination and pose variation. Then, the MATP-STM extracts the texture patterns and DESURF-LOP extracts the relevant key features of the facial points. The PPCSO algorithm selects the relevant features from the MATP-STM feature set to speed up the classification. The employment of LSTM-CNN predicts the required label for the facial expressions.The major key findings of the proposed work are clear image analysis, effective handling of pose/illumination variations and the facial alignment. The proposed MAOP-DL validates its effectiveness on two standard databases such as CK+ and MMI regarding various metrics and confirm their assurance of wide applicability in recent applications.
KeywordStm Surf Cnn Lstm
DOIhttp://dx.doi.org/10.1016/j.patrec.2017.06.025
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21195
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation1.CRIPAC & NLPR, CASIA, PR China
2.University of Chinese Academy of Sciences, PR China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jain Deepak Kumar,Zhang Zhang,Huang Kaiqi,et al. Multi angle optimal pattern-based deep learning for automatic facial expression recognition[J]. Pattern Recognition Letters,2017(XX):1-9.
APA Jain Deepak Kumar,Zhang Zhang,Huang Kaiqi,&Kaiqi Huang.(2017).Multi angle optimal pattern-based deep learning for automatic facial expression recognition.Pattern Recognition Letters(XX),1-9.
MLA Jain Deepak Kumar,et al."Multi angle optimal pattern-based deep learning for automatic facial expression recognition".Pattern Recognition Letters .XX(2017):1-9.
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