Abstract
Face recognition is an active research area in biometric authentication, which has gained more attention among researchers due to the availability of feasible technologies, including mobile solutions. However, the human facial images are high dimensional, so the dimensionality reduction methods are often adapted for face recognition. However, the facial images are corrupted by the noise and hard to label in the data collection phase. In this study, a new GOA-DCNN model is proposed for face recognition to address those issues. Initially, the face images are collected from two online datasets FEI face and ORL. Next, modified local binary pattern (MLBP) and speeded up robust features (SURF) are used to extract the feature vectors from the collected facial images. The extracted feature values are optimized using grasshopper optimization algorithm (GOA) to decrease the dimensionality of data or to select the optimal feature vectors. At last, deep convolutional neural network (DCNN) was applied to classify the person’s facial image. The experimental result proves that the proposed model improved recognition accuracy up to 1.78–8.90% compared to the earlier research works such as improved kernel linear discriminant analysis and probabilistic neural networks (IKLDA + PNN) and convolutional neural network (CNN) with pre-trained VGG-Face.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Gao G, Yu Y, Yang M, Huang P, Ge Q, Yue D (2020) Multi-scale patch based representation feature learning for low-resolution face recognition. Appl Soft Comput 106183
Mi JX, Sun Y, Lu J (2020) Robust supervised sparse representation for face recognition. Cognit Syst Res 62:10–22
Zhang G, Porikli F, Sun H, Sun Q, Xia G, Zheng Y (2020) Cost-sensitive joint feature and dictionary learning for face recognition. Neurocomputing 391:177–188
Orrù G, Marcialis GL, Roli F (2020) A novel classification-selection approach for the self-updating of template-based face recognition systems. Pattern Recognit 100:107121
Shakeel MS, Lam KM (2019) Deep-feature encoding-based discriminative model for age-invariant face recognition. Pattern Recognit 93:442–457
Kas M, Ruichek Y, Messoussi R (2018) Mixed neighborhood topology cross decoded patterns for image-based face recognition. Expert Syst Appl 114:119–142
Nikan S, Ahmadi M (2018) A modified technique for face recognition under degraded conditions. J Vis Commun Image Rep 55:742–755
Gan H (2018) A noise-robust semi-supervised dimensionality reduction method for face recognition. Optik 157:858–865
Ouyang A, Liu Y, Pei S, Peng X, He M, Wang Q (2020) A hybrid improved kernel LDA and PNN algorithm for efficient face recognition. Neurocomputing 393:214–222
Faraji MR, Qi X (2018) Face recognition under varying illuminations with multi-scale gradient maximum response. Neurocomputing 308:87–100
Elmahmudi A, Ugail H (2019) Deep face recognition using imperfect facial data. Future Gener Comput Syst 99:213–225
Li H, Suen CY (2016) Robust face recognition based on dynamic rank representation. Pattern Recognit 60:13–24
Li Y, Zheng W, Cui Z, Zhang T (2018) Face recognition based on recurrent regression neural network. Neurocomputing 297:50–58
Tang J, Su Q, Su B, Fong S, Cao W, Gong X (2020) Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition. Comput Methods Progr Biomed 105622
Dong X, Zhang H, Sun J, Wan W (2017) A two-stage learning approach to face recognition. J Vis Commun Image Rep 43:21–29
Roy H, Bhattacharjee D (2018) A novel local wavelet energy mesh pattern (LWEMeP) for heterogeneous face recognition. Image Vis Comput 72:1–13
Deng X, Da F, Shao H, Jiang Y (2020) A multi-scale three-dimensional face recognition approach with sparse representation-based classifier and fusion of local covariance descriptors. Comput Electr Eng 85:106700
Vijayakumar T (2019) Comparative study of capsule neural network in various applications. J Artif Intell 1(01):19–27
Thomaz CE, Giraldi GA (2010) A new ranking method for principal components analysis and its application to face image analysis. Image Vis Comput 28:902–913
Jin Z, Yang JY, Hu ZS, Lou Z (2001) Face recognition based on the uncorrelated discriminant transformation. Pattern Recogn 34:1405–1416
FEI face dataset. https://fei.edu.br/~cet/facedatabase.html
ORL dataset. https://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Veerashetty, S., Patil, N.B. (2021). Texture-Based Face Recognition Using Grasshopper Optimization Algorithm and Deep Convolutional Neural Network. In: Bindhu, V., Tavares, J.M.R.S., Boulogeorgos, AA.A., Vuppalapati, C. (eds) International Conference on Communication, Computing and Electronics Systems. Lecture Notes in Electrical Engineering, vol 733. Springer, Singapore. https://doi.org/10.1007/978-981-33-4909-4_4
Download citation
DOI: https://doi.org/10.1007/978-981-33-4909-4_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4908-7
Online ISBN: 978-981-33-4909-4
eBook Packages: EngineeringEngineering (R0)