Age-Invariant Face Recognition Using Triangle Geometric Features

Whilst facial recognition systems are vulnerable to different acquisition conditions, most notably lighting effects and pose variations, their particular level of sensitivity to facial aging effects is yet to be researched. The face recognition vendor test (FRVT) 2012's annual statement estimated deterioration in the performance of face recognition systems due to facial aging. There was about 5% degradation in the accuracies of the face recognition systems for each single year age difference between a test image and a probe image. Consequently, developing an age-invariant platform continues to be a significant requirement for building an effective facial recognition system. The main objective of this work is to address the challenge of facial aging which affects the performance of facial recognition systems. Accordingly, this work presents a geometrical model that is based on extracting a number of triangular facial features. The proposed model comprises a total of six triangular areas connecting and surrounding the main facial features (i.e. eyes, nose and mouth). Furthermore, a set of thirty mathematical relationships are developed and used for building a feature vector for each sample image. The areas and perimeters of the extracted triangular areas are calculated and used as inputs for the developed mathematical relationships. The performance of the system is evaluated over the publicly available face and gesture recognition research network (FG-NET) face aging database. The performance of the system is compared with that of some of the state-of-the-art face recognition methods and state-of-the-art age-invariant face recognition systems. Our proposed system yielded a good performance in term of classification accuracy of more than 94%.

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RBS: A ROBUST BIMODAL SYSTEM FOR FACE RECOGNITION

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194007003379 ◽

2007 ◽

Vol 17 (04) ◽

pp. 497-513 ◽

Cited By ~ 8

Author(s):

ANDREA F. ABATE ◽

MICHELE NAPPI ◽

DANIEL RICCIO ◽

GENOVEFFA TORTORA

Keyword(s):

Face Recognition ◽

State Of The Art ◽

Recognition Task ◽

Iterated Function Systems ◽

Related Literature ◽

Ear Recognition ◽

Iterated Function ◽

The Face ◽

The Subject ◽

Recognition Systems

During the last few years, many algorithms have been proposed in particular for face recognition using classical 2-D images. However, it is necessary to deal with occlusions when the subject is wearing sunglasses, scarves and such. In the same way, ear recognition is arising as a new promising biometric for people recognition, even if the related literature appears to be somewhat underdeveloped. In this paper, several hybrid face/ear recognition systems are investigated. The system is based on IFS (Iterated Function Systems) theory that are applied on both face and ear resulting in a bimodal architecture. One advantage is that the information used for the indexing and recognition task of face/ear can be made local, and this makes the method more robust to possible occlusions. The distribution of similarities in the input images is exploited as a signature for the identity of the subject. The amount of information provided by each component of the face and the ear image has been assessed, first independently and then jointly. At last, results underline that the system significantly outperforms the existing approaches in the state of the art.

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Kernelized Heterogeneity-Aware Cross-View Face Recognition

Frontiers in Artificial Intelligence ◽

10.3389/frai.2021.670538 ◽

2021 ◽

Vol 4 ◽

Author(s):

Tejas I. Dhamecha ◽

Soumyadeep Ghosh ◽

Mayank Vatsa ◽

Richa Singh

Keyword(s):

Face Recognition ◽

Case Studies ◽

Near Infrared ◽

State Of The Art ◽

Facial Features ◽

Digital Photo ◽

Face Matching ◽

The Face ◽

Improved Performance ◽

Heterogeneity Variance

Cross-view or heterogeneous face matching involves comparing two different views of the face modality such as two different spectrums or resolutions. In this research, we present two heterogeneity-aware subspace techniques, heterogeneous discriminant analysis (HDA) and its kernel version (KHDA) that encode heterogeneity in the objective function and yield a suitable projection space for improved performance. They can be applied on any feature to make it heterogeneity invariant. We next propose a face recognition framework that uses existing facial features along with HDA/KHDA for matching. The effectiveness of HDA and KHDA is demonstrated using both handcrafted and learned representations on three challenging heterogeneous cross-view face recognition scenarios: (i) visible to near-infrared matching, (ii) cross-resolution matching, and (iii) digital photo to composite sketch matching. It is observed that, consistently in all the case studies, HDA and KHDA help to reduce the heterogeneity variance, clearly evidenced in the improved results. Comparison with recent heterogeneous matching algorithms shows that HDA- and KHDA-based matching yields state-of-the-art or comparable results on all three case studies. The proposed algorithms yield the best rank-1 accuracy of 99.4% on the CASIA NIR-VIS 2.0 database, up to 100% on the CMU Multi-PIE for different resolutions, and 95.2% rank-10 accuracies on the e-PRIP database for digital to composite sketch matching.

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Metrological characterization of 3D biometric face recognition systems in actual operating conditions

ACTA IMEKO ◽

10.21014/acta_imeko.v6i1.392 ◽

2017 ◽

Vol 6 (1) ◽

pp. 33 ◽

Cited By ~ 1

Author(s):

Giovanni Betta ◽

Domenico Capriglione ◽

Mariella Corvino ◽

Alberto Lavatelli ◽

Consolatina Liguori ◽

...

Keyword(s):

Face Recognition ◽

Operating Conditions ◽

Classification Systems ◽

Appearance Model ◽

Facial Features ◽

Railway Stations ◽

Input Face ◽

The Face ◽

Recognition Systems ◽

Stereo Triangulation

<p>Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few.<br />In such scenarios, the architectures based on stereo vision and 3D reconstruction of the face are going to assume a predominant role because they can generally assure a better reliability than solutions based on a single camera (which make use of a single image instead of a couple of images). To realize such systems, different architectures can be considered by varying the positioning of the pair of cameras with respect to the face of the subject to be identified, as well as both kind and resolution of camera considered. These parameters can affect the correct decision rate of the system in classifying the input face, especially in presence of image uncertainty.<br />In this paper, several 3D architectures differing in camera specifications and geometrical positioning of the camera pair (with respect to the input face) are realized and compared. The detection of facial features in the images is made by adopting a popular method based on the Active Appearance Model (AAM) algorithm. 3D position of facial features is then obtained by means of stereo triangulation. The performance of the realized systems has been compared in terms of sensitivity to the quantities of influence and related uncertainty, and of typical indexes for the analysis of classification systems. Main results of such comparison show that the best performance can be reached by reducing the distance between cameras and subject to be identified and by minimizing the horizontal angle between the plane containing the camera pair axis and the face to be identified.</p>

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Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

Sustainability ◽

10.3390/su13126900 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6900

Author(s):

Jonathan S. Talahua ◽

Jorge Buele ◽

P. Calvopiña ◽

José Varela-Aldás

Keyword(s):

Facial Recognition ◽

Face Mask ◽

Recognition System ◽

Classification Model ◽

World Health ◽

The Face ◽

Recognition Systems ◽

Facial Recognition System ◽

Health Organization ◽

Face Detector

In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.

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Performance Analysis of Pose Invariant Face Recognition Approaches in Unconstrained Environments

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2015010104 ◽

2015 ◽

Vol 5 (1) ◽

pp. 66-81 ◽

Cited By ~ 1

Author(s):

M. Parisa Beham ◽

S. M. Mansoor Roomi ◽

J. Alageshan ◽

V. Kapileshwaran

Keyword(s):

Face Recognition ◽

Nearest Neighbor ◽

State Of The Art ◽

Recognition Algorithm ◽

K Nearest Neighbor ◽

Research Issues ◽

Histograms Of Oriented Gradients ◽

Recognition Phase ◽

The Face ◽

Pose Variation

Face recognition and authentication are two significant and dynamic research issues in computer vision applications. There are many factors that should be accounted for face recognition; among them pose variation is a major challenge which severely influence in the performance of face recognition. In order to improve the performance, several research methods have been developed to perform the face recognition process with pose invariant conditions in constrained and unconstrained environments. In this paper, the authors analyzed the performance of a popular texture descriptors viz., Local Binary Pattern, Local Derivative Pattern and Histograms of Oriented Gradients for pose invariant problem. State of the art preprocessing techniques such as Discrete Cosine Transform, Difference of Gaussian, Multi Scale Retinex and Gradient face have also been applied before feature extraction. In the recognition phase K- nearest neighbor classifier is used to accomplish the classification task. To evaluate the efficiency of pose invariant face recognition algorithm three publicly available databases viz. UMIST, ORL and LFW datasets have been used. The above said databases have very wide pose variations and it is proved that the state of the art method is efficient only in constrained situations.

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Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College

Symmetry ◽

10.3390/sym12020307 ◽

2020 ◽

Vol 12 (2) ◽

pp. 307 ◽

Cited By ~ 1

Author(s):

Ngo Tung Son ◽

Bui Ngoc Anh ◽

Tran Quy Ban ◽

Le Phuong Chi ◽

Bui Dinh Chien ◽

...

Keyword(s):

Face Recognition ◽

Motion Blur ◽

Security Monitoring ◽

Flexible System ◽

Lighting Conditions ◽

Satisfactory Performance ◽

Measurement Results ◽

The Face ◽

Recognition Systems

Face recognition (FR) has received considerable attention in the field of security, especially in the use of closed-circuit television (CCTV) cameras in security monitoring. Although significant advances in the field of computer vision are made, advanced face recognition systems provide satisfactory performance only in controlled conditions. They deteriorate significantly in the face of real-world scenarios such as lighting conditions, motion blur, camera resolution, etc. This article shows how we design, implement, and conduct the empirical comparisons of machine learning open libraries in building attendance taking (AT) support systems using indoor security cameras called ATSS. Our trial system was deployed to record the appearances of 120 students in five classes who study on the third floor of FPT Polytechnic College building. Our design allows for flexible system scaling, and it is not only usable for a school but a generic attendance system with CCTV. The measurement results show that the accuracy is suitable for many different environments.

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DTFA-Net: Dynamic and Texture Features Fusion Attention Network for Face Antispoofing

Complexity ◽

10.1155/2020/5836596 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Xin Cheng ◽

Hongfei Wang ◽

Jingmei Zhou ◽

Hui Chang ◽

Xiangmo Zhao ◽

...

Keyword(s):

Face Recognition ◽

Face Detection ◽

Feature Fusion ◽

Texture Features ◽

Replay Attack ◽

Attention Network ◽

Detection Failure ◽

The Face ◽

Recognition Systems ◽

Common Face

For face recognition systems, liveness detection can effectively avoid illegal fraud and improve the safety of face recognition systems. Common face attacks include photo printing and video replay attacks. This paper studied the differences between photos, videos, and real faces in static texture and motion information and proposed a living detection structure based on feature fusion and attention mechanism, Dynamic and Texture Fusion Attention Network (DTFA-Net). We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. We conducted experiments on the CASIA-MFSD and Replay Attack Databases. According to experiments, the DTFA-Net proposed in this paper achieved 6.9% EER on CASIA and 2.2% HTER on Replay Attack that was comparable to other methods.

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Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

Scientific Programming ◽

10.1155/2020/6385281 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Ahmed Jawad A. AlBdairi ◽

Zhu Xiao ◽

Mohammed Alghaili

Keyword(s):

Neural Network ◽

Deep Learning ◽

Face Recognition ◽

Convolutional Neural Network ◽

State Of The Art ◽

Research Community ◽

Facial Features ◽

New Model ◽

Image Dataset ◽

Deep Cnn

The interest in face recognition studies has grown rapidly in the last decade. One of the most important problems in face recognition is the identification of ethnics of people. In this study, a new deep learning convolutional neural network is designed to create a new model that can recognize the ethnics of people through their facial features. The new dataset for ethnics of people consists of 3141 images collected from three different nationalities. To the best of our knowledge, this is the first image dataset collected for the ethnics of people and that dataset will be available for the research community. The new model was compared with two state-of-the-art models, VGG and Inception V3, and the validation accuracy was calculated for each convolutional neural network. The generated models have been tested through several images of people, and the results show that the best performance was achieved by our model with a verification accuracy of 96.9%.

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Face Recognition Using Completed Local Ternary Pattern (CLTP) Texture Descriptor

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i3.pp1594-1601 ◽

2017 ◽

Vol 7 (3) ◽

pp. 1594 ◽

Cited By ~ 2

Author(s):

Taha H. Rassem ◽

Nasrin M. Makbol ◽

Sam Yin Yee

Keyword(s):

Face Recognition ◽

Recognition Task ◽

Texture Features ◽

Classification Rate ◽

Local Ternary Pattern ◽

Japanese Female ◽

The Face ◽

Recognition Systems ◽

High Recognition Accuracy ◽

Event Image

Nowadays, face recognition becomes one of the important topics in the computer vision and image processing area. This is due to its importance where can be used in many applications. The main key in the face recognition is how to extract distinguishable features from the image to perform high recognition accuracy. Local binary pattern (LBP) and many of its variants used as texture features in many of face recognition systems. Although LBP performed well in many fields, it is sensitive to noise, and different patterns of LBP may classify into the same class that reduces its discriminating property. Completed Local Ternary Pattern (CLTP) is one of the new proposed texture features to overcome the drawbacks of the LBP. The CLTP outperformed LBP and some of its variants in many fields such as texture, scene, and event image classification. In this study, we study and investigate the performance of CLTP operator for face recognition task. The Japanese Female Facial Expression (JAFFE), and FEI face databases are used in the experiments. In the experimental results, CLTP outperformed some previous texture descriptors and achieves higher classification rate for face recognition task which has reached up 99.38% and 85.22% in JAFFE and FEI, respectively.

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A Web-Based Mobile Attendance System with Facial Recognition Feature

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v14i05.13311 ◽

2020 ◽

Vol 14 (05) ◽

pp. 193

Author(s):

Noradila Nordin ◽

Nurul Husna Mohd Fauzi

Keyword(s):

Face Recognition ◽

Mobile Devices ◽

Conventional Method ◽

Facial Recognition ◽

Recognition System ◽

Web Based ◽

Face Recognition System ◽

The Face ◽

System Prototype

Attendance marking in a classroom is one of the methods used to track the student’s presence in the lecture. The conventional method that is being enforced has shown to be vulnerable, inaccurate and time-consuming especially in a large classroom. It is difficult to identify absentees and proxy attendees based on the conventional attendance marking method. In order to overcome the challenges faced in the conventional method, a web-based mobile attendance system with facial recognition feature is proposed. It incorporated the existing mobile devices with a camera and the face recognition system to allow the attendance system to be used in classrooms automatically and efficiently with minor implementation requirements. The system prototype received positive responses from the volunteers who tested the system to replace the conventional attendance marking.

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