Secured Optimal Cost Approach for Bimodal Deep Face Recognition in IoT and Its Applications

2018 ◽  
pp. 163-175
Author(s):  
Madhavi Gudavalli ◽  
Vidaysree P ◽  
S Viswanadha Raju ◽  
Surekha Borra
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns ◽  
Commercial Law ◽  
Optimal Cost ◽  
North West ◽  
Cost Approach ◽  
Iot Applications ◽  
Emerging Trends ◽  
The Face ◽  
Face Datasets

This chapter proposes an optimal cost security approach for the current and emerging trends in the Engineering centric IoT applications that offer an optimized infrastructure and human safety through bimodal deep face recognition. Human face determines the person identity that reveals information like age, gender, emotions, attractiveness and others. Face recognition attracted researchers to enhance its performance because of its potential usage in several commercial, law enforcement, government and video surveillance applications in which individuals perceive each other. In this chapter, authors propose a new secured optimal cost approach for deep face recognition based on feature level fusion of bi-features extracted through unsupervised deep learner, Autoencoder and Local Binary Patterns (LBP) respectively. The dimensionality of fused feature map is reduced and protected through Forward Error Correction (FEC) technique. An efficient optimal cost region matcher (OCRM) is accomplished with Canny edge detector to maximize the face recognition accuracy. OCRM uses north-west corner rule of the transportation problem that fulfills the Monge property. The experimental results demonstrate the superiority of the proposed face recognition system over unimodal systems (Autoencoder and LBP alone) when tested on ORL and Real face datasets with OCRM matcher which is interfaced through diverse IoT applications.

Novel Technique for 3D Face Recognition Using Anthropometric Methodology

2018 ◽  
Vol 9 (1) ◽  
pp. 60-77 ◽  
Author(s):  
Souhir Sghaier ◽  
Wajdi Farhat ◽  
Chokri Souani
Keyword(s):  
Face Recognition ◽  
Learning Algorithm ◽  
Recognition Performance ◽  
Experimental Tests ◽  
3D Face Recognition ◽  
3D Face ◽  
Area Of Interest ◽  
The Face ◽  
Face Datasets ◽  
Improved Technique

This manuscript presents an improved system research that can detect and recognize the person in 3D space automatically and without the interaction of the people's faces. This system is based not only on a quantum computation and measurements to extract the vector features in the phase of characterization but also on learning algorithm (using SVM) to classify and recognize the person. This research presents an improved technique for automatic 3D face recognition using anthropometric proportions and measurement to detect and extract the area of interest which is unaffected by facial expression. This approach is able to treat incomplete and noisy images and reject the non-facial areas automatically. Moreover, it can deal with the presence of holes in the meshed and textured 3D image. It is also stable against small translation and rotation of the face. All the experimental tests have been done with two 3D face datasets FRAV 3D and GAVAB. Therefore, the test's results of the proposed approach are promising because they showed that it is competitive comparable to similar approaches in terms of accuracy, robustness, and flexibility. It achieves a high recognition performance rate of 95.35% for faces with neutral and non-neutral expressions for the identification and 98.36% for the authentification with GAVAB and 100% with some gallery of FRAV 3D datasets.

scholarly journals Facial Recognition for Drunk People Using Thermal Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Agustin Sancen-Plaza ◽  
Luis M. Contreras-Medina ◽  
Alejandro Israel Barranco-Gutiérrez ◽  
Carlos Villaseñor-Mora ◽  
Juan J Martínez-Nolasco ◽  
...  
Keyword(s):  
Face Recognition ◽  
Surface Temperature ◽  
Thermal Imaging ◽  
Visible Spectrum ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Thermal Images ◽  
Average Percentage ◽  
The Face ◽  
Thermal Face

Face recognition using thermal imaging has the main advantage of being less affected by lighting conditions compared to images in the visible spectrum. However, there are factors such as the process of human thermoregulation that cause variations in the surface temperature of the face. These variations cause recognition systems to lose effectiveness. In particular, alcohol intake causes changes in the surface temperature of the face. It is of high relevance to identify not only if a person is drunk but also their identity. In this paper, we present a technique for face recognition based on thermal face images of drunk people. For the experiments, the Pontificia Universidad Católica de Valparaíso-Drunk Thermal Face database (PUCV-DTF) was used. The recognition system was carried out by using local binary patterns (LBPs). The LBP features were obtained from the bioheat model from thermal image representation and a fusion of thermal images and a vascular network extracted from the same image. The feature vector for each image is formed by the concatenation of the LBP histogram of the thermogram with an anisotropic filter and the fused image, respectively. The proposed technique has an average percentage of 99.63% in the Rank-10 cumulative classification; this performance is superior compared to using LBP in thermal images that do not use the bioheat model.

scholarly journals A Novel Genetic Convolutional Neuro Multi-Fuzzy Techniques for Newborn Face Recognition

2021 ◽  
Vol 12 (6) ◽  
pp. 1037-1046
Author(s):  
T. Arul Raj, Et. al.
Keyword(s):  
Fuzzy Logic ◽  
Face Recognition ◽  
Recognition System ◽  
Biometric Recognition ◽  
Face Recognition System ◽  
Neuro Fuzzy ◽  
The Face ◽  
Face Datasets ◽  
Fuzzy Techniques ◽  
Fully Connected

Advances in technology have made life simpler in today's society by supplying us with a variety of emerging demands lacking By assessing the progressive stability of biometric recognition accuracy for newborns, biometric recognition can be used to recognize missing newborns and prevent them from being switched in higher-level hospitals.. Recognizing and authenticating newborns is a major problem in many hospitals. The face recognition system does an outstanding job of identifying and authenticating the newborn. To answer these concerns, create a face recognition device for newborns. The proposed approach improves picture consistency on a newborn's face. Our objectives are to propose a genetic, convolutional neural network, and fuzzy logic-based automated framework for newborn face recognition. As a paradigm GCNMF is suggested for real-world newborn face recognition. Convolutional, pooling, and fully-connected layers, as well as a Neuro Fuzzy layer, form the Inherited Convolutional Neuro Multi-Fuzzy. The model employs hereditary, convolutional neural networks, and fuzzy logic to deal with ambiguity and imprecision in the input configuration representation. The efficacy and outcomes of the recommended method are then analyzed using newborn face datasets and the Genetic Convolutional Neuro Multi-Fuzzy (GCNMF) Approach.

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

scholarly journals RECOGNITION OF MULTI-VIEW HUMAN FACES BASED ON MACHINE INTELLIGENCE USING KLT ALGORITHM

2017 ◽  
Vol 5 (3) ◽  
pp. 123-134
Author(s):  
Haripriya K ◽  
Ramya Lakshmi V. ◽  
Rajeswari S ◽  
Rama T ◽  
Vinothini K.R
Keyword(s):  
Face Recognition ◽  
Video Retrieval ◽  
Three Dimensional ◽  
Local Binary Patterns ◽  
Machine Intelligence ◽  
Or Efficiency ◽  
Wide Range ◽  
The Face ◽  
Fully Automatic ◽  
Human Faces

Nowadays Image Processing has become a proficient domain due to the prolific techniques like face detection and face recognition. They play an important role in our society due to their use in wide range of applications such as surveillance, security, banking, and multimedia. One of major challenges faced in this technique of face recognition is difficulty in handling arbitrary pose variations in three dimensional representations. In video retrieval system, many approaches have been developed for recognition across pose variations and to assume the face poses to be known. These constraints made it semi-automatic. In this paper we propose a fully automatic method for multi-view face recognition of improving the accuracy or efficiency using local binary patterns. It uses tree-based data structure to create sub-grids. In this system we use KLT algorithm to detect and extract features automatically by using Eigen vectors and estimation of hessian value.

scholarly journals Domain Adaptation for Robust Face Recognition Using Transfer Kernel Learning

2020 ◽  
Author(s):  
João Renato Manesco ◽  
Aparecido Marana
Keyword(s):  
Face Recognition ◽  
Domain Adaptation ◽  
Kernel Learning ◽  
The Face ◽  
The Difference ◽  
Face Datasets ◽  
Robust Face Recognition ◽  
The Moment ◽  
Facial Images ◽  
Accuracy Rates

In the last decades, for reasons of safety or convenience, biometric characteristics are increasingly being used to identify individuals who wish to have access to systems or places, and facial features are one of the most used characteristics for this purpose. For biometric identification to be effective, the recognition accuracy rates must be high. However, these rates can be very low depending on the difference (displacement) between the domain of the images stored in the database of the biometric system (source images) and the images used at the moment of identification (target images). In this work, we evaluated the performance of a domain adaptation method called Transfer Kernel Learning (TKL) in the face recognition problem. Results obtained in our experiments on two face datasets, ARFace and FRGC, corroborates that TKL is suitable for domain adaptation and that it is capable of improving significantly the accuracy rates of face recognition, even when considering facial images with occlusions, variations in illumination and complex backgrounds.

scholarly journals Local Binary Patterns Histograms (LBPH) Based Face Recognition

2019 ◽  
Vol 9 (1) ◽  
pp. 1088-1091
Keyword(s):  
Face Recognition ◽  
Individual Recognition ◽  
Local Binary Patterns ◽  
Physical Structure ◽  
Manhattan Distance ◽  
Person Identification ◽  
Chi Square ◽  
Research Article ◽  
The Face ◽  
The Difference

The human face has been broadly used in computer vision field for individual recognition. The face recognition is one of the secure ways to protect the data over the internet. In this paper we use (LBPH) Local Binary Patterns Histogram based Face Recognition. We use Yale face database for experiment and it contains 165 grey images in the GIF format of 15 person and 11 image per person and in this experiment we use only normal image in 180*180 at grey scale images and in this research article in the verification phase the difference between two histograms are calculated by Chi-square distance, Manhattan distance. The proposed technique has achieved TSR=98.8% in Chi-square and TSR=98.5% in Manhattan distance parameter. Person Identification using their physical structure or behavioral characteristic is known as the biometric.

scholarly journals An Integrated Technique for Face Sketch Recognition Using DCNN

2019 ◽  
Vol 8 (10) ◽  
pp. 3133-3140
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Local Binary Patterns ◽  
Image Features ◽  
Local Descriptor ◽  
Multi Scale ◽  
Face Sketch Recognition ◽  
Tchebichef Moments ◽  
The Face ◽  
Integrated Technique

Face Recognition (FR) is considered as one of the chief use in the investigation of criminals. In the majority of the cases, information about the criminal is not available. In such situations, sketch artist draw the sketch of the guess with the oral explanation provided by the eyewitness. These sketches can then be matched manually against mug shot photos. This process is time-consuming. Hence there require a method that efficiently goes with composite sketches to the gallery of mug shot databases. Thus the proposed system uses a scheme for matching composite sketch and photo images, photo image features are extracted and fused to train the system. Composite Sketch feature is matched with face photo images. Feature extraction (FE) is done using Multi-Scale Local Binary Patterns (MLBP) Tchebichef Moments and Multiscale Circular Weber Local Descriptor (MCWLD), Principal Component Analysis (PCA) is used for fusion of extracted features, DCNN used as a classifier to recognize the face. The experiments are conducted using PRIP-HDC dataset and the proposed system gives good accuracy in face recognition.

scholarly journals Comparative Study of LBPH and Haar features in Real Time Recognition Under Varying Light Intensities

2020 ◽  
Vol 9 (4s) ◽  
pp. 27-29
Keyword(s):  
Face Recognition ◽  
Comparative Study ◽  
Local Binary Patterns ◽  
Training Set ◽  
Open Environment ◽  
Wide Range ◽  
Haar Features ◽  
The Face ◽  
Access Control System ◽  
Set Up

Face recognition is a commonly used biometric and has a wide range of applications. We used an access control system that integrates face recognition technology. This paper discusses two algorithms that have been used in the face detection, Haar features and Local Binary Patterns Histogram (LBPH). The experimental set up is done in an open environment using OpenCV library. Comparative study has been made between these two algorithms based on parameters like illumination and hit rate. For the testing, the same training set and samples were used.

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