Dynamic Training Data Dropout for Robust Deep Face Recognition

2021 ◽  
pp. 1-1
Author(s):  
Yaoyao Zhong ◽  
Weihong Deng ◽  
Han Fang ◽  
Jiani Hu ◽  
Dongyue Zhao ◽  
...  
Keyword(s):  
Face Recognition ◽  
Training Data ◽  
Data Dropout

A TIED-MIXTURE 2D HMM FACIAL IMAGE RETRIEVAL SYSTEM

2004 ◽  
Vol 13 (05) ◽  
pp. 1133-1146
Author(s):  
H. OTHMAN ◽  
T. ABOULNASR
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Training Data ◽  
Expected Improvement ◽  
Model Case ◽  
Face Recognition System ◽  
Image Retrieval System ◽  
The Face ◽  
Institute Of Technology ◽  
Observation Space

In this paper, the effect of mixture tying on a second-order 2D Hidden Markov Model (HMM) is studied as applied to the face recognition problem. While tying HMM parameters is a well-known solution in the case of insufficient training data that leads to nonrobust estimation, it is used here to improve the overall performance in the small model case where the resolution in the observation space is the main problem. The fully-tied-mixture 2D HMM-based face recognition system is applied to the facial database of AT&T and the facial database of Georgia Institute of Technology. The performance of the proposed 2D HMM tied-mixture system is studied and the expected improvement is confirmed.

SUBSPACE LEARNING BASED ON LAPLACIAN EIGENMAPS AND LDA FOR FACE RECOGNITION

2006 ◽  
Vol 03 (01) ◽  
pp. 45-51
Author(s):  
YANWEI PANG ◽  
ZHENGKAI LIU ◽  
YUEFANG SUN
Keyword(s):  
Face Recognition ◽  
Subspace Learning ◽  
Dimensional Subspace ◽  
Training Data ◽  
Objective Functions ◽  
Recognition Method ◽  
Laplacian Eigenmaps ◽  
Linear Discriminant ◽  
Structure Information ◽  
Low Dimensional

Subspace-based face recognition method aims to find a low-dimensional subspace of face appearance embedded in a high-dimensional image space. The differences between different methods lie in their different motivations and objective functions. The objective function of the proposed method is formed by combining the ideas of linear Laplacian eigenmaps and linear discriminant analysis. The actual computation of the subspace reduces to a maximum eigenvalue problem. Major advantage of the proposed method over traditional methods is that it utilizes both local manifold structure information and discriminant information of the training data. Experimental results on the AR face databases demonstrate the effectiveness of the proposed method.

The Comparison of Laplacianfaces QR Decomposition and Linear Discriminant Analysis QR Decomposition Algorithm for Face Recognition System on Orthogonal Subspace

2014 ◽  
Vol 71 (1) ◽  
Author(s):  
Purbandini Purbandini
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Selection Process ◽  
Recognition System ◽  
Qr Decomposition ◽  
Training Data ◽  
Distance Method ◽  
Linear Discriminant ◽  
Face Recognition System

Development of an optimal face recognition system will greatly depend on the characteristics of the selection process are as a basis to pattern recognition. In the characteristic selection process, there are 2 aspects that will be of mutual influence such the reduction of the amount of data used in the classification aspects and increasing discrimination ability aspects. Linear Discriminat Analysis method helps presenting the global structure while Laplacianfaces method is one method that is based on appearance (appearance-based method) in face recognition, in which the local manifold structure presented in the adjacency graph mapped from the training data points. Linear Discriminant Analysis QR decomposition has a computationally low cost because it has small dimensions so that the efficiency and scalability are very high when compared with algorithms of other Linear Discriminant Analysis methods. Laplacianfaces QR decomposition was a algorithm to obtain highly speed and accuracy, and tiny space to keep data on the face recognition. This algorithm consists of 2 stages. The first stage maximizes the distance of between-class scatter matrices by using QR decomposition and the second stage to minimize the distance of within-class scatter matrices. Therefore, it is obtained an optimal discriminant in the data. In this research, classification using the Euclidean distance method. In these experiments using face databases of the Olivetti-Att-ORL, Bern and Yale. The minimum error was achieved with the Laplacianfaces QR decomposition and Linear Discriminant Analysis QR decomposition are 5.88% and 9.08% respectively. 

A Kernel Two-Phase Test Sample Sparse Representation for Face Recognition

2015 ◽  
Vol 30 (01) ◽  
pp. 1656001
Author(s):  
Zhonghua Liu ◽  
Jiexin Pu ◽  
Yong Qiu ◽  
Moli Zhang ◽  
Xiaoli Zhang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Recognition Performance ◽  
Mapping Function ◽  
Feature Space ◽  
Test Sample ◽  
Training Data ◽  
Two Phase ◽  
Kernel Parameter ◽  
Representation Method

Sparse representation is a new hot technique in recent years. The two-phase test sample sparse representation method (TPTSSR) achieved an excellent performance in face recognition. In this paper, a kernel two-phase test sample sparse representation method (KTPTSSR) is proposed. Firstly, the input data are mapped into an implicit high-dimensional feature space by a nonlinear mapping function. Secondly, the data are analyzed by means of the TPTSSR method in the feature space. If an appropriate kernel function and the corresponding kernel parameter are selected, a test sample can be accurately represented as the linear combination of the training data with the same label information of the test sample. Therefore, the proposed method could have better recognition performance than TPTSSR. Experiments on the face databases demonstrate the effectiveness of our methods.

scholarly journals SISTEM PRESENSI MAHASISWA OTOMATIS PADA ZOOM MEETING MENGGUNAKAN FACE RECOGNITION DENGAN METODE CONVULITIONAL NEURAL NETWORK BERBASIS WEB

2021 ◽  
Vol 5 (2) ◽  
pp. 785-793
Author(s):  
Sujud Satwikayana ◽  
Suryo Adi Wibowo ◽  
Nurlaily Vendyansyah
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Virus Disease ◽  
Training Data ◽  
Corona Virus

Dalam rangka pencegahan perkembangan dan penyebaran Corona Virus Disease (COVID-19), Kementerian Pendidikan dan Kebudayaan mengeluarkan SE Mendikbud Tahun 2020 tentang Pembelajaran secara Daring dan Bekerja dari Rumah dalam rangka Pencegahan Penyebaran COVID-19. Pembelajaran secara daring dan bekerja dari rumah bagi para tenaga pendidik merupakan perubahan yang harus dilakukan untuk tetap mengajar mahasiswa. Ketika melakukan pembelajaran secara daring tentunya memerlukan media sebagai sarananya. Survei terbaru yang dilakukan oleh Lembaga Arus Survei Indonesia (ASI) terkait penggunaan media video call dalam pembelajaran daring, mayoritas publik menggunakan aplikasi Zoom (57,2 %), disusul Google Meet (18,5 %), Cisco Webex (8,3 %), U Meet Me (5,0 %), Microsoft Teams (2,0 %), dan lainnya (2,2 %). Sisanya 6,9 % mengaku tidak tahu atau tidak jawab. Presensi sangat penting untuk mengetahui dan mengontrol kehadiran peserta didik dalam proses belajar mengajar. Saat ini presensi dalam perkuliahan daring masih dilakukan secara manual. Untuk itu perlu dibuat sistem pencatatan kehadiran berbasis face recognition secara otomatis. Dalam penelitian ini metode yang digunakan untuk face recognition adalah Convolutional Neural Network (CNN). Metode diimplementasikan dengan bantuan library Keras untuk proses training data. Hasil dari penelitian ini adalah sistem berbasis web yang dapat mendeteksi wajah mahasiswa yang berpartisipasi dalam ruang Zoom meeting. Pengujian yang dilakukan kepada 10 orang relawan munggunakan model hasil training data metode  CNN dari total 150 kali uji coba, total benar sebanyak 138 kali dan total salah sebanyak 12 kali, menunjukkan kinerja pengenalan wajah meraih rata-rata tingkat akurasi benar sebesar 92,00 % dan salah sebesar 8,00 % yang berarti sudah menghasilkan kecocokan yang baik.

Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) for hyperspectral image based face recognition in real time door locking applications

2021 ◽  
pp. 1-11
Author(s):  
Ashok Kumar Rai ◽  
Radha Senthilkumar ◽  
A. Kanan
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recurrent Neural Network ◽  
Hyperspectral Image ◽  
Selection Process ◽  
Facial Feature ◽  
Recognition System ◽  
Training Data ◽  
Search Optimization ◽  
Feature Extraction And Selection

Face recognition is one of the best applications of computer recognition and recent smart house applications. Therefore, it draws considerable attention from researchers. Several face recognition algorithms have been proposed in the last decade, but these methods did not give the efficient outcome. Therefore, this work introduces a novel constructive training algorithm for smart face recognition in door locking applications. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) Strategy is applied to face recognition application. The steady preparing system has been utilized where the training designs are adapted steadily and are divided into completely different modules. The facial feature process works on global and local features. After the feature extraction and selection process, employ the improved classifier followed by the Framed Recurrent Neural Network classification technique. Finally, the face image based on the feature library can be identified. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization starts with a single training pattern using Bidirectional Encoder Representations from Transformers (BERT) model. During network training, the Training Data (TD) decrease the Mean Square Error (MSE) while the matching process increases the algorithms generated which are trapped at the local minimum. The training data have been trained to increase the number of input forms (one after the other) until all the forms are selected and trained. An FRNN-MDSO based face recognition system is built, and face recognition is tested using hyperspectral Database parameters. The simulation results indicate that the proposed method acquires the associate grade optimum design of FRNN with MDSO methodology using the present constructive algorithm and prove the proposed FRNN-MDSO method’s effectiveness compared to the conventional architecture methods.

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