Performance Analysis of the Feedforward and SOM Neural Networks in the Face Recognition Problem

2007 ◽  
Author(s):  
M.I. Chacon M. ◽  
P. Rivas-Perea
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Performance Analysis ◽  
Recognition Problem ◽  
The Face

Nonrigid Registration Algorithm in the Face Recognition Problem

2021 ◽  
Vol 66 (12) ◽  
pp. 1452-1459
Author(s):  
A. Yu. Makovetskii ◽  
V. I. Kober ◽  
S. M. Voronin ◽  
A. V. Voronin ◽  
V. N. Karnaukhov ◽  
...  
Keyword(s):  
Face Recognition ◽  
Recognition Problem ◽  
Nonrigid Registration ◽  
Registration Algorithm ◽  
The Face

On solving the face recognition problem with one training sample per subject

2006 ◽  
Vol 39 (9) ◽  
pp. 1746-1762 ◽  
Author(s):  
Jie Wang ◽  
K.N. Plataniotis ◽  
Juwei Lu ◽  
A.N. Venetsanopoulos
Keyword(s):  
Face Recognition ◽  
Training Sample ◽  
Recognition Problem ◽  
The Face

FACE RECOGNITION IN COLOUR IMAGES USING NEURAL NETWORKS AND GENETIC ALGORITHMS

2005 ◽  
Vol 05 (01) ◽  
pp. 55-67 ◽  
Author(s):  
STEPHEN KARUNGARU ◽  
MINORU FUKUMI ◽  
NORIO AKAMATSU
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Template Matching ◽  
Distance Measure ◽  
Facial Feature ◽  
Training Data ◽  
Segmentation Method ◽  
Facial Feature Extraction ◽  
Matching Process ◽  
The Face

In this paper, a system that can automatically detect and recognise frontal faces is proposed. Three methods are used for face recognition; neural network, template matching and distance measure. One of the main problems encountered when using neural networks for face recognition is insufficient training data. This problem arises because, in most cases, only one image per subject is available. Therefore, amongst the objectives is to solve this problem by "increasing" the data available from the original image using several preprocesses, for example, image mirroring, colour and edges information, etc. Moreover, template matching is not trivial because of differences in the template shapes and sizes. In this work, template matching is aided by a genetic algorithm to automatically test several positions around the target and automatically adjust the size of the template as the matching process progresses. Distance measure method depends heavily on good facial feature extraction results. The image segmentation method applied matches such demand. The face colour information is represented using YIQ and the XYZ colour spaces. The effectiveness of the proposed method is verified by performing computer simulations. Two sets of databases were used. Database1 consists of 267 faces from the Oulu university database and database2 (for comparision purposes) consists of 250 faces from the ORL database.

scholarly journals Customized orthogonalization via deflation algorithm with applications in face recognition

2014 ◽  
Vol 30 (2) ◽  
pp. 231-238
Author(s):  
LACRAMIOARA (LITA) GRECU ◽  
◽  
ELENA PELICAN ◽  
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Dimension Reduction ◽  
Linear Algebra ◽  
Numerical Linear Algebra ◽  
Recognition Problem ◽  
Topical Issue ◽  
Standard Algorithm ◽  
The Face ◽  
Reduction Methods

The face recognition problem is a topical issue in computer vision. In this paper we propose a customized version of the orthogonalization via deflation algorithm to tackle this problem. We test the new proposed algorithm on two datasets: the well-known ORL dataset and an own face dataset, CTOVF; also, we compare our results (in terms of rate recognition and average quiery time) with the outcome of a standard algorithm in this class (dimension reduction methods using numerical linear algebra tools).

scholarly journals UFaceNet: Research on Multi-Task Face Recognition Algorithm Based on CNN

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 268
Author(s):  
Huoyou Li ◽  
Jianshiun Hu ◽  
Jingwen Yu ◽  
Ning Yu ◽  
Qingqiang Wu
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Recognition Algorithm ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
Model Calculations ◽  
Keypoint Detection ◽  
In Vivo Detection ◽  
The Face

With the application of deep convolutional neural networks, the performance of computer vision tasks has been improved to a new level. The construction of a deeper and more complex network allows the face recognition algorithm to obtain a higher accuracy, However, the disadvantages of large computation and storage costs of neural networks limit the further popularization of the algorithm. To solve this problem, we have studied the unified and efficient neural network face recognition algorithm under the condition of a single camera; we propose that the complete face recognition process consists of four tasks: face detection, in vivo detection, keypoint detection, and face verification; combining the key algorithms of these four tasks, we propose a unified network model based on a deep separable convolutional structure—UFaceNet. The model uses multisource data to carry out multitask joint training and uses the keypoint detection results to aid the learning of other tasks. It further introduces the attention mechanism through feature level clipping and alignment to ensure the accuracy of the model, using the shared convolutional layer network among tasks to reduce model calculations amount and realize network acceleration. The learning goal of multi-tasking implicitly increases the amount of training data and different data distribution, making it easier to learn the characteristics with generalization. The experimental results show that the UFaceNet model is better than other models in terms of calculation amount and number of parameters with higher efficiency, and some potential areas to be used.

scholarly journals Face Recognition Using Livenessnet

2021 ◽  
pp. 143-148
Author(s):  
Shajahan K ◽  
Rathish Rai D ◽  
Ravishankara
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Access Control ◽  
Smart Card ◽  
Deep Neural Networks ◽  
Digital Technologies ◽  
Recognition Method ◽  
Public Places ◽  
The Face ◽  
Face Spoofing

Every person's face is unique, although have the same structure such as noise, eyes, lips, etc. but it can vary strikingly. It’s within this variance which lies in the distinguishing characteristics that can be used to identify one person from another. Face recognition is a popular concept which is commonly used in surveillance cameras at public places for security purposes. With the advancement of digital technologies, the demand for security to provide access control is increasing. It uses various methods of authentication to keep all details secure, such as a system focused on encrypted user name & password, smart card, biometrics, etc. The “Face Recognition using DNN with LivenessNet” presents a face recognition method based on deep neural networks for liveness. Any algorithm is considered to be efficient only if it is robust and accurate. It provides accurate results with face spoofing quickly and efficiently. The main advantage of using this technique is identifying the uniqueness in the datasets by capturing the real-time face data through different modes & jitter. It provides accurate face recognition model which can be used for safety and security purpose.

scholarly journals Real Time Face Detection using DNN with Raspberry Pi 4

2019 ◽  
Vol 8 (4) ◽  
pp. 9771-9778
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Human Brain ◽  
Raspberry Pi ◽  
Application Range ◽  
Emerging Trends ◽  
The Face ◽  
Image Pixels

The concept of face recognition is in the emerging trends nowadays ,because of its wide application range .Usually ,the face recognition is used in the surveillance ,security and Here, Face recognition is used to allocate attendance for a candidate.Deep neural networks is a group of artificial intelligence entirely based on neural networks, because the algorithm will imitate the human brain, so deep learning can be a kind of imitation of the human brain.Local Binary Pattern (LBP) is a basic but also very advanced creaminess operator that names image pixels through thresholding every pixel's district and considers the outcome as just a binary number.If the recognised face is not authenticated or if unauthorised person is identified by the system ,it immediately alerts the server and the classroom door remains closed. In this project we have created our own database with faculty and students of our section using Logitech C270 HD camera with resolution of 720p/30fps

Face Detection With Face Mask for COVID-19 Pandemic Using Neural Networks

2022 ◽  
pp. 210-223
Author(s):  
Nitish Devendra Warbhe ◽  
Rutuja Rajendra Patil ◽  
Tarun Rajesh Shrivastava ◽  
Nutan V. Bansode
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Face Detection ◽  
Face Mask ◽  
Detection Technique ◽  
Facial Features ◽  
Feature Points ◽  
The People ◽  
Biometric Systems ◽  
The Face

The COVID-19 virus can be spread through contact and contaminated surfaces; therefore, typical biometric systems like password and fingerprint are unsafe. Face recognition solutions are safer without any need of touching any device. During the COVID-19 situation as all of the people are advised to wear masks on their faces, the existing face detection technique is not able to identify the person with face occlusion. The fraudsters and thieves take advantage of this scenario and misuse the face mask, favoring them to be able to steal and commit various crimes without being identified. Face recognition methods fail to detect or recognize the face as half of the face is masked and the features are suppressed. Face recognition requires the visibility of major facial features for face normalization, orientation correction, and recognition. Thus, the chapter focuses on the facial recognition based on the feature points surrounding the eye region rather than taking the whole face as a parameter.

Face Recognition Based on Wavelet Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 216-219
Author(s):  
Hong Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Human Face ◽  
Recognition Method ◽  
Human Face Recognition ◽  
The Face ◽  
Simulation Testing

It's well known that the technology of human face recognition has become a hot topicin pattern recognition field. Though a lot of progress has been made by many researchersthese years, many key problems still have to be solved in order to popularize the application of face recognition because of the complexity of face recognition. The background, development and main methods of face recognition are introducedfirstly in this paper, then a face recognition method which is based on wavelet transform,KL transform and BP neural networks is used in the paper.Here the face feature extraction includes wavelet transform and KL transform.Moreover,the recognition classifier is BP neural networks.The simulation testing in the paper holds good recognition rate.

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