Abstract
Nowadays, digital protection has become greater prominence for daily digital activities. It’s far vital for people to keep new passwords in their minds and carry additional playing cards with themselves. Such practices but are getting much less stable and realistic, as a consequence leading to a growing interest in techniques associated with biometrics systems. Biometrics structures keep the bodily residences of humans in electronic surroundings and enable them to be recognized by using the stored electronic records while needed. Several different face recognition and authentication methods had been proposed. However, most of the implementation is done using Principal Component Analysis(PCA) and measuring the recognition costs. In our work we propose a new approach for fast face recognition by applying deep learning techniques. We look at the outcome of recognition subject to the various components of the face and the eyes, mouth, nose, and brow. Distinctive features are extracted from the face, which is achieved using GreyLevel CoOccurrenceMatrix(GLCM). The GLCM method is a useful feature for feature extraction because of its excessive overall stabilizing local comparison. Lastly, dataset training and feature classification of the facial data’s are carried out using Multi-Class Artificial neuralnetworks(MCANN) and Adaboost in which every distinctive face within the database is distinguished. Later the facial identification tool is tested on four groups of databases, AT&T, YALE B, VGG, and CASIA. In the end, we will apply analysis to measure accuracy and precision.
Similar content being viewed by others
References
Collobert R, Weston J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning. Proc. 25th Int Conf Mach Learn pp 1601 67
Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:24932537
Florian Schroff DKJP (2015) FaceNet: a unified embedding for face recognition and clustering. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Girshick R, Donahue J, Darrell T, Malik J (2016) Region-based convolutionalnetworks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142158
Huang GB, Ramesh M, Berg T, Learned Miller E (2007) Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical report, Technical Report 07–49, University of Massachusetts, Amherst
Liang M, Hu X (2015) Recurrent convolutional neural network for object recognition, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp 33673375
Mohamed MAK, El-Sayed Yarub A, Estaitia A (2013) Automated Edge Detection Using Convolutional Neural Network. Int J Adv Comput Sci Appl 4(10):1117
Sudeep DA Thepade D (2018) Face gender recognition using multi layer perceptron with OTSU segmentation. Fourth international conference on computing communication control and automation (ICCUBEA)
Viola P, Jones M (2001) Robust real-time face detection. In Proc Eighth IEEE Int Conf Computer Vision ICCV 2001, volume 2, page 747
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Begum, N., Mustafa, A.S. A novel approach for multimodal facial expression recognition using deep learning techniques. Multimed Tools Appl 81, 18521–18529 (2022). https://doi.org/10.1007/s11042-022-12238-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-12238-y