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.

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 Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Naeem Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Anzar Mahmood ◽  
Sohail Razzaq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Alignment Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Norm Minimization ◽  
Wide Range ◽  
The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

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.

SUBSPACES VERSUS SUBMANIFOLDS: A COMPARATIVE STUDY IN SMALL SAMPLE SIZE PROBLEM

2009 ◽  
Vol 23 (03) ◽  
pp. 463-490 ◽  
Author(s):  
HONG HUANG ◽  
JIANWEI LI ◽  
HAILIANG FENG
Keyword(s):  
Face Recognition ◽  
Comparative Study ◽  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Locally Linear Embedding ◽  
Small Sample Size Problem ◽  
Size Problem ◽  
The Face ◽  
Locally Linear

Automatic face recognition is a challenging problem in the biometrics area, where the dimension of the sample space is typically larger than the number of samples in the training set and consequently the so-called small sample size problem exists. Recently, neuroscientists emphasized the manifold ways of perception, and showed the face images may reside on a nonlinear submanifold hidden in the image space. Many manifold learning methods, such as Isometric feature mapping, Locally Linear Embedding, and Locally Linear Coordination are proposed. These methods achieved the submanifold by collectively analyzing the overlapped local neighborhoods and all claimed to be superior to such subspace methods as Eigenfaces and Fisherfaces in terms of classification accuracy. However, in literature, no systematic comparative study for face recognition is performed among them. In this paper, we carry out a comparative study in face recognition among them, and the study considers theoretical aspects as well as simulations performed using CMU PIE and FERET face databases.

The System of Face Recognition Design

2014 ◽  
Vol 644-650 ◽  
pp. 3943-3946
Author(s):  
Xiao Bin Yu ◽  
Zi Qiao Li ◽  
Wen Qiang Ke ◽  
Rui Peng Li ◽  
Kai Xiong
Keyword(s):  
Face Recognition ◽  
Skin Color ◽  
Color Space ◽  
Color Model ◽  
Face Images ◽  
Accurate Detection ◽  
Haar Features ◽  
The Face ◽  
The Media ◽  
Skin Color Model

The technology of face recognition is the media to face images as the identity of the face recognition system.Through the choice of color space and the establishment of skin color model, give a rough detection for the human's image, then use the face Haar features getting more accurate detection.

Deep Learning in Age-invariant Face Recognition: A Comparative Study

2020 ◽  
Author(s):  
Muhammad Sajid ◽  
Nouman Ali ◽  
Naeem Iqbal Ratyal ◽  
Muhammad Usman ◽  
Faisal Mehmood Butt ◽  
...  
Keyword(s):  
Face Recognition ◽  
Comparative Study ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
Face Images ◽  
Effective Age ◽  
The Face ◽  
Feature Normalization ◽  
The Comparative Study ◽  
Learned Features

Abstract This paper presents comparative evaluation of an application of deep convolutional neural networks (dCNNs) to age invariant face recognition. To this end, we use four distinct dCNN models, the AlexNet, VGGNet, GoogLeNet and ResNet. We assess their performance to recognize face images across aging variations, firstly by fine-tuning the models and secondly using them as face feature extractor. We also suggest a novel synthesized aging augmentation technique suitable for age-invariant face recognition using dCNNs. The face recognition experiments are conducted on three challenging FG-NET, MORPH and LAG aging datasets, and results are benchmarked with a simple CNN. The comparative study allows us to answer (i) when and why transfer learning or feature extraction strategies are useful in age-invariant face recognition scenarios, (ii) the potential of aging synthesized augmentation to increase accuracy and (iii) the choice of appropriate feature normalization and distance metrics to be used with deeply learned features. The extensive experiments, and valuable insights presented in this study can be extended to the design of effective age-invariant face recognition algorithms.

scholarly journals Development of a secured room access system based on face recognition using Raspberry Pi and Android based smartphone

2018 ◽  
Vol 197 ◽  
pp. 11008 ◽  
Author(s):  
Asep Najmurrokhman ◽  
Kusnandar Kusnandar ◽  
Arief Budiman Krama ◽  
Esmeralda Contessa Djamal ◽  
Robbi Rahim
Keyword(s):  
Face Recognition ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Security Issues ◽  
Access System ◽  
The Face ◽  
Access Control System ◽  
Haar Cascade ◽  
Door Lock ◽  
Access Rights

Security issues are an important part of everyday life. A vital link in security chain is the identification of users who will enter the room. This paper describes the prototype of a secured room access control system based on face recognition. The system comprises a webcam to detect faces and a solenoid door lock for accessing the room. Every user detected by the webcam will be checked for compatibility with the database in the system. If the user has access rights then the solenoid door lock will open and the user can enter the room. Otherwise, the data will be sent to the master user via Android-based smartphone that installed certain applications. If the user is recognized by the master user, then the solenoid door lock will be opened through the signal sent from the smartphone. However, if the user is not recognized, then the buzzer will alert. The main control circuit on this system is Raspberry pi. The software used is OpenCV Library which is useful to display and process the image produced by webcam. In this paper, we employ Haar Cascade Classifier in an image processing of user face to render the face detection with high accuracy.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document