Improved Face and Facial Expression Recognition Based on a Novel Local Gradient Neighborhood

Computing efficiency is a key in biometric identification systems for automatic facial expression recognition. It was integrated within advanced pattern recognition as an excellent paradigm while users shifted towards underlying patterns. Most existing face recognition models suffer from a low recognition rate and significant execution time. To overcome these drawbacks, we propose a new Local Gradient Neighborhood (LGN) descriptor for effective face and facial expression recognition. Firstly, the LGN components obtained by applying LGN for each block of the face image which is represented by 9-size vector. Secondly, the system concatenates features vectors of different blocks to obtain the final feature vector for the face image. Finally, it applies SVM and KNN techniques to classify the input images. Unlike other similar works, the new proposed descriptor is evaluated on two benchmarks, for face recognition and facial expression recognition respectively. The experimental results show an excellent recognition rate and fast execution time. The recognition rate for the ORL face database is 98.50% and the recognition rate for the JAFEE database is 84.28%. Subject Categories and Descriptors: [I.4.7 Feature Measurement]; [I.5 PATTERN RECOGNITION]: Neural nets General Terms: Local Gradient Neighborhood, Face Expression Recognition, Classification, SVM, Feature Extraction

[1]  Lynette I. Millett,et al.  Biometric Recognition: Challenges and Opportunities , 2010 .

[2]  Lei Wang,et al.  Generalized 2D principal component analysis for face image representation and recognition , 2005, Neural Networks.

[3]  Moi Hoon Yap,et al.  Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation , 2016, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

[4]  V. Vaidehi,et al.  Person Authentication Using Face Detection , 2008 .

[5]  Sergios Theodoridis,et al.  Machine Learning: A Bayesian and Optimization Perspective , 2015 .

[6]  Yi-Hsuan Yang,et al.  Machine Recognition of Music Emotion: A Review , 2012, TIST.

[7]  Chih-Jen Lin,et al.  Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..

[8]  Ping Liu,et al.  Facial Expression Recognition via a Boosted Deep Belief Network , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Rabia Jafri,et al.  A Survey of Face Recognition Techniques , 2009, J. Inf. Process. Syst..

[10]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[11]  Jean Meunier,et al.  Emotion recognition using dynamic grid-based HoG features , 2011, Face and Gesture 2011.

[12]  Jieping Ye,et al.  Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems , 2005, J. Mach. Learn. Res..

[13]  Hélio Pedrini,et al.  Recognition of occluded facial expressions based on CENTRIST features , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Lin Li,et al.  Facial Expression Recognition Using Histogram Sequence of Local Gabor Gradient Code-Horizontal Diagonal and Oriented Gradient Descriptor , 2017 .

[15]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Mario Baum,et al.  Handbook Of Biometrics , 2016 .

[17]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[18]  Wen-Shiung Chen,et al.  A novel personal biometric authentication technique using human iris based on fractal dimension features , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[19]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[20]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[21]  Cheng Li,et al.  Pose-Robust Face Recognition via Deep Residual Equivariant Mapping , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  Yong Cheng,et al.  Facial expression recognition algorithm using LGC based on horizontal and diagonal prior principle , 2014 .

[23]  Yuichi Ohta,et al.  Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor , 2009, ICDP.

[24]  Jorge Cadima,et al.  Principal component analysis: a review and recent developments , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[25]  Emam Hossain,et al.  Automated Facial Expression Recognition Using Gradient-Based Ternary Texture Patterns , 2013 .

[26]  M. Oravec,et al.  Face recognition methods based on principal component analysis and feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[27]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[28]  Oksam Chae,et al.  Local directional pattern variance (ldpv): a robust feature descriptor for facial expression recognition , 2012, Int. Arab J. Inf. Technol..

[29]  Alberto Del Bimbo,et al.  Deep Learning from 3DLBP Descriptors for Depth Image Based Face Recognition , 2019, 2019 International Conference on Biometrics (ICB).

[30]  Sridha Sridharan,et al.  Face recognition using fractal codes , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[31]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[32]  W. L. Woo,et al.  Facial Expression Recognition using Local Gabor Gradient Code-Horizontal Diagonal Descriptor , 2015 .

[33]  Banupriya,et al.  SURVEY ON FACE RECOGNITION , 2014 .