Abstract
Facial expressions convey human emotions in an expressive way. The development of an automated system to recognize the facial expressions is the difficult task. Automatic Facial Expression Recognition (FER) is an imperative process that leads to next-generation Human-Machine Interaction (HMI) for clinical practice and behavioral description. Segment detection and the extraction of relevant information from the images are the major issues to design an effective FER system. The creation of the suitable system that addresses these issues is the basic stage to achieve the accurate HMI models. The in-depth information analysis and maximization of labeled database are the real problems in the domain of FER approaches. A novel framework based on Square-Based Diagonal Pattern (SBDP) method on Geometric model called Geometric Appearance Models (GAM) has been presented through this paper that extracts the in-depth detailed of the features. The framework adopts the co-training by using detailed information from RGB-D images. The performance analysis of proposed SBDP-GAM regarding identification rate, sensitivity, accuracy and error rates with the RGB-D images shows the effectiveness diagonal patterns in facial expression identification. The comparative analysis of proposed SBDP-GAM model with the traditional methods regarding the recognition rate, error rate and F-score and on RGB-D images from EURECOMM database states the effectiveness of the proposed method. Moreover, the comparison of proposed SBDP-GAM with existing Support Vector Machine (SVM) regarding the acceptance rate (FAR, FRR, GAR) measures for biographer RGB-D image database proves the effectiveness of SBDP-GAM in FER applications.
References
Baltrušaitis T, Robinson P, Morency LP (2014) Continuous Conditional Neural Fields for Structured Regression. In:ECCV. 593–608
Bettadapura V (2012) Face expression recognition and analysis: the state of the art. arXiv:1203.6722
Bianco S, Cusano C, Schettini R (2015) Color Constancy Using CNNs. arXiv:1504.04548
Chen J, Takiguchi T, Ariki Y (2015) Facial expression recognition with a multithreaded cascade of rotation-invariant HOG. In: ACII. 636–642
Cheng Y, Zhao X, Huang K, Tan T (2014) Semi-supervised Learning for RGB-D Object Recognition. In: ICPR. 2377–2382
Coates A, Lee H, Ng AY (2010) An analysis of single-layer networks in unsupervised feature learning, Ann Arbor. 1001: 2
Fulcher BD, Jones NS (2014) Highly comparative feature-based time-series classification. In: IEEE Trans. KDE. 3026–3037
Ghosh D, Ari S (2015) Static Hand Gesture Recognition Using Mixture of Features and SVM Classifier. In: CSNT, 2015
Goswami G, Bharadwaj S, Vatsa M, Singh R (2013) On RGB-D face recognition using Kinect. In:BTAS 2013
Jiang B, Jia K (2013) Semi-supervised facial expression recognition algorithm on the condition of multi-pose. In: JIHMSD. 138–146
Lemaire P, Ardabilian M, Liming C, Daoudi M (2013) Fully automatic 3D facial expressionrecognition using differential mean curvature maps and histograms of oriented gradients. In:FG 2013
Liu M, Li S, Shan S, Chen X (2013) Enhancing Expression Recognition in the Wild with Unlabeled Reference Data. In: ACCV. 577–588
Madhu M, Amutha R (2012) Face recognition using gray level co-occurrence matrix and snapshot method of the Eigenface. In: IJEIT, 482–488
Maghraby AE, Abdalla M, Enany O, El Nahas M (2014) Detect and Analyze Face Parts Information using Viola-Jones and Geometric Approaches. In: IJCA. 23–28
Nicolaou MA, Gunes H, Pantic M (2012) Output-associative RVM regression for dimensional and continuous emotion prediction. In: Image and Vision Computing. 186–196
Ramirez Rivera A, Rojas C, Oksam C (2013) Local Directional Number Pattern for Face Analysis: Face and Expression Recognition. In: IEEETrans. IP, pp. 1740–1752
Reddy P, Reddy BE, Kumar VV (2013) Fuzzy-Based Image Dimensionality Reduction Using Shape Primitives for Efficient Face Recognition. In: JDMT, 2013
Romero P, Cid F, Núnez P (2013) A novel real-time facial expression recognition system based on candide-3 reconstruction model. In: WAF 2013
Ruan J, Yin J, Chen Q, Chen G (2014) Facial Expression Recognition Based on Gabor Wavelet Transform and Relevance Vector Machine. In: JICS, 2013
Rui M, Kose N, Dugelay JL (2014) KinectFaceDB: A Kinect Database for Face Recognition. IEEETrans. SMCS. 1534–1548
Sandbach G, Zafeiriou S, Pantic M, Yin L (2012) Static and dynamic 3D facial expression recognition: a comprehensive survey. Image Vis Comput:683–697
Shen F, Xu Y, Liu L, Yang Y, Huang Z, Shen HT (2018) Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization, in IEEE Transactions on Pattern Analysis and Machine Intelligence. doi: https://doi.org/10.1109/TPAMI.2018.2789887
Soleymani M, Pantic M, Pun T (2012) Multimodal emotion recognition in response to videos. In: IEEE Trans. AC. 211–223
Srivastava S, Joshi N, Gaur M (2014) A Review Paper on Feature Selection Methodologies and Their Applications. In: IJCSNS, pp. 78
Wong Y, Harandi MT, Sanderson C (2014) On robust face recognition via sparse coding: the good, the bad and the ugly. IET Biometrics 3:176–189
Wright J, Yang AY , Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation, IEEE transactions on pattern analysis and machine intelligence. 31: 210–227
Yang M, Zhang L (2010) Gabor feature based sparse representation for face recognition with gabor occlusion dictionary,” in European conference on computer vision. 448–461
Yang M, Zhang L, Yang J, Zhang D (2011) Robust sparse coding for face recognition,” in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference. 625–632
Yang W, Li J, Zheng, Xu (2018) A Nuclear Norm Based Matrix Regression Based Projections Method for Feature Extraction, in IEEE Access, 6:7445–7451. doi: https://doi.org/10.1109/ACCESS.2017.2784800
Yongqiang L, Shangfei W, Yongping Z, Qiang J (2013) Simultaneous Facial Feature Tracking and Facial Expression Recognition. In: IEEETrans. IP. 2559–2573
Yu Q, Yin Y, Yang G, Ning Y, Li Y (2012) Face and Gait Recognition Based on Semi-supervised Learning. In: Pattern Recognition. 284–291
Acknowledgements
Foremost, I would like to express my sincere gratitude to Dr. Deepak Kumar Jain, Institute of Automation, Chinese Academy of Sciences, Beijing, China for the continuous support of this research. His patience, motivation, enthusiasm, and immense knowledge helped me in all the time of research project.
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Jain, N., Kumar, S. & Kumar, A. Effective approach for facial expression recognition using hybrid square-based diagonal pattern geometric model. Multimed Tools Appl 78, 29555–29571 (2019). https://doi.org/10.1007/s11042-019-7325-x
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DOI: https://doi.org/10.1007/s11042-019-7325-x