scholarly journals Optimal hog cell to image ratio for robust multi-sensor face recognition systems

2019 ◽  
Vol 16 (3) ◽  
pp. 387-403
Author(s):  
Milos Pavlovic ◽  
Branka Stojanovic ◽  
Ranko Petrovic ◽  
Snezana Puzovic ◽  
Srdjan Stankovic
Keyword(s):  
Face Recognition ◽  
Visible Light ◽  
Environmental Conditions ◽  
Recognition Accuracy ◽  
Thermal Infrared ◽  
Feature Descriptor ◽  
Image Size ◽  
Accurate Identification ◽  
Recognition Systems ◽  
Facial Images

The main problem for modern visible light face recognition has been accurate identification under variable environmental conditions. Thermal infrared facial images utilization in face recognition systems can provide a solution for problems related to uncontrolled environmental conditions, especially to those caused by illumination limitations. This paper compares the results of the use of visible light and thermal infrared imagery for face recognition based on the HOG feature descriptor. In particular, the paper suggests an optimal HOG cell to image size ratio in order to improve recognition accuracy and reduce computational complexity. Performance statistics are presented on facial images with different facial expressions. The obtained results support the conclusion that recognition with thermal infrared images is more robust and that fusion of sensors should be included for improving recognition accuracy.

scholarly journals MagNet: Detecting Digital Presentation Attacks on Face Recognition

2021 ◽  
Vol 4 ◽  
Author(s):  
Akshay Agarwal ◽  
Richa Singh ◽  
Mayank Vatsa ◽  
Afzel Noore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Error Rates ◽  
Attack Detection ◽  
Limited Attention ◽  
Feature Descriptor ◽  
Security Risk ◽  
Learning Networks ◽  
Social Media Platforms ◽  
Recognition Systems

Presentation attacks on face recognition systems are classified into two categories: physical and digital. While much research has focused on physical attacks such as photo, replay, and mask attacks, digital attacks such as morphing have received limited attention. With the advancements in deep learning and computer vision algorithms, several easy-to-use applications are available where with few taps/clicks, an image can be easily and seamlessly altered. Moreover, generation of synthetic images or modifying images/videos (e.g. creating deepfakes) is relatively easy and highly effective due to the tremendous improvement in generative machine learning models. Many of these techniques can be used to attack the face recognition systems. To address this potential security risk, in this research, we present a novel algorithm for digital presentation attack detection, termed as MagNet, using a “Weighted Local Magnitude Pattern” (WLMP) feature descriptor. We also present a database, termed as IDAgender, which consists of three different subsets of swapping/morphing and neural face transformation. In contrast to existing research, which utilizes sophisticated machine learning networks for attack generation, the databases in this research are prepared using social media platforms that are readily available to everyone with and without any malicious intent. Experiments on the proposed database, FaceForensic database, GAN generated images, and real-world images/videos show the stimulating performance of the proposed algorithm. Through the extensive experiments, it is observed that the proposed algorithm not only yields lower error rates, but also provides computational efficiency.

scholarly journals Age Invariant Face Recognition using Convolutional Neural Network

2018 ◽  
Vol 8 (4) ◽  
pp. 2126 ◽  
Author(s):  
Mrudula Nimbarte ◽  
Kishor Bhoyar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Accuracy ◽  
Classification Algorithms ◽  
The Arts ◽  
Single Structure ◽  
Facial Images ◽  
Selection Of

<span>In the recent years, face recognition across aging has become very popular and challenging task in the area of face recognition.  Many researchers have contributed in this area, but still there is a significant gap to fill in. Selection of feature extraction and classification algorithms plays an important role in this area. Deep Learning with Convolutional Neural Networks provides us a combination of feature extraction and classification in a single structure. In this paper, we have presented a novel idea of 7-Layer CNN architecture for solving the problem of aging for recognizing facial images across aging. We have done extensive experimentations to test the performance of the proposed system using two standard datasets FGNET and MORPH</span><span>(Album II). Rank-1 recognition accuracy of our proposed system is 76.6% on FGNET and 92.5% on MORPH</span><span>(Album II). Experimental results show the significant improvement over available state-of- the-arts with the proposed CNN architecture and the classifier.</span>

scholarly journals Modelling of Facial Images for Analysis of Recognition System

2021 ◽  
Vol 2107 (1) ◽  
pp. 012041
Author(s):  
Assyakirin M H ◽  
Shafriza Nisha B ◽  
Haniza Y ◽  
Fathinul Syahir A S ◽  
Muhammad Juhairi A S
Keyword(s):  
Face Recognition ◽  
Performance Analysis ◽  
Facial Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Background Intensity ◽  
Face Recognition System ◽  
Recognition Systems ◽  
Human Faces ◽  
Facial Images

Abstract Face recognition is categorized as a biometric technology that employs the use of computer ability in image processing to detect and recognize human faces. Face recognition system has numerous applications for many purposes such as for access control, law enforcement and surveillance thus this system is dominant in present technology. Generally, face recognition system become more advance in term of the accuracy and implementation. However, there are a few parameters that effects the accuracy of recognition system for examples, the pose invariant, illumination effect, size of image and noise tolerance. Even though there are a number of systems were already available in the literature, the complete understanding of their performances are relatively limited. This is due to many systems focused on a narrow application band – therefore, a comprehensive analysis are needed in order to understand their performances leading to establishing the conditions for successful face recognition system. In this paper we developed a synthetic model to represent facial images to be used as a platform for performance analysis of facial recognition systems. The model includes 5 face types with the ability to vary all parameters that are affecting recognition performance – measurement noise, face size and face-background intensity differences. The model is important as it provide an avenue for performance analysis of facial recognition systems.

Finding Facial Emotions From the Clutter Scenes Using Zernike Moments-Based Convolutional Neural Networks

2021 ◽  
pp. 241-265
Author(s):  
Wencan Zhong ◽  
Vijayalakshmi G. V. Mahesh ◽  
Alex Noel Joseph Raj ◽  
Nersisson Ruban
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Facial Expression ◽  
Convolutional Neural Networks ◽  
Zernike Moments ◽  
The Other ◽  
Recognition Systems ◽  
Facial Images ◽  
Surveillance Applications

Finding faces in the clutter scenes is a challenging task in automatic face recognition systems as facial images are subjected to changes in the illumination, facial expression, orientation, and occlusions. Also, in the cluttered scenes, faces are not completely visible and detecting them is essential as it is significant in surveillance applications to study the mood of the crowd. This chapter utilizes the deep learning methods to understand the cluttered scenes to find the faces and discriminate them into partial and full faces. The work proves that MTCNN used for detecting the faces and Zernike moments-based kernels employed in CNN for classifying the faces into partial and full takes advantage in delivering a notable performance as compared to the other techniques. Considering the limitation of recognition on partial face emotions, only the full faces are preserved, and further, the KDEF dataset is modified by MTCNN to detect only faces and classify them into four emotions. PatternNet is utilized to train and test the modified dataset to improve the accuracy of the results.

scholarly journals A Study on Presentation Attack Detection in Thermal Infrared

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3988
Author(s):  
Marcin Kowalski
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Infrared Imaging ◽  
Thermal Infrared ◽  
Attack Detection ◽  
Thermal Infrared Imaging ◽  
3D Printed ◽  
Recognition Systems ◽  
Thermal Face ◽  
Presentation Attack Detection

Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation attacks using printed and displayed facial photographs, 3D-printed, custom flexible 3D-latex and silicone masks is provided. The paper presents the intensity analysis of thermal energy distribution for specific facial landmarks during long-lasting experiments. Thermalization impact, as well as varying the subject’s state due to physical effort on presentation attack detection are investigated. A new thermal face spoofing dataset is introduced. Finally, a two-step deep learning-based method for the detection of presentation attacks is presented. Validation results of a set of deep learning methods across various presentation attack instruments are presented.

scholarly journals Two Factors in Face Recognition: Whether You Know the Person’s Face and Whether You Share the Person’s Race

Perception ◽  
2021 ◽  
pp. 030100662110140
Author(s):  
Xingchen Zhou ◽  
A. M. Burton ◽  
Rob Jenkins
Keyword(s):  
Face Recognition ◽  
Factorial Design ◽  
Performance Measures ◽  
Recognition Accuracy ◽  
The Other ◽  
Familiarity Effect ◽  
Race Effect ◽  
Two Factors ◽  
Unfamiliar Face

One of the best-known phenomena in face recognition is the other-race effect, the observation that own-race faces are better remembered than other-race faces. However, previous studies have not put the magnitude of other-race effect in the context of other influences on face recognition. Here, we compared the effects of (a) a race manipulation (own-race/other-race face) and (b) a familiarity manipulation (familiar/unfamiliar face) in a 2 × 2 factorial design. We found that the familiarity effect was several times larger than the race effect in all performance measures. However, participants expected race to have a larger effect on others than it actually did. Face recognition accuracy depends much more on whether you know the person’s face than whether you share the same race.

Hierarchical Scheme for Vehicle Make and Model Recognition

2021 ◽  
pp. 036119812110197
Author(s):  
Chaoqing Wang ◽  
Junlong Cheng ◽  
Yuefei Wang ◽  
Yurong Qian
Keyword(s):  
Intelligent Transportation Systems ◽  
Recognition Accuracy ◽  
Transportation Systems ◽  
Feature Descriptor ◽  
Vehicle Recognition ◽  
Public Dataset ◽  
Classification Of Images ◽  
Model Recognition ◽  
Hierarchical Scheme

A vehicle make and model recognition (VMMR) system is a common requirement in the field of intelligent transportation systems (ITS). However, it is a challenging task because of the subtle differences between vehicle categories. In this paper, we propose a hierarchical scheme for VMMR. Specifically, the scheme consists of (1) a feature extraction framework called weighted mask hierarchical bilinear pooling (WMHBP) based on hierarchical bilinear pooling (HBP) which weakens the influence of invalid background regions by generating a weighted mask while extracting features from discriminative regions to form a more robust feature descriptor; (2) a hierarchical loss function that can learn the appearance differences between vehicle brands, and enhance vehicle recognition accuracy; (3) collection of vehicle images from the Internet and classification of images with hierarchical labels to augment data for solving the problem of insufficient data and low picture resolution and improving the model’s generalization ability and robustness. We evaluate the proposed framework for accuracy and real-time performance and the experiment results indicate a recognition accuracy of 95.1% and an FPS (frames per second) of 107 for the framework for the Stanford Cars public dataset, which demonstrates the superiority of the method and its availability for ITS.

Multimodal biometric system using deep learning based on face and finger vein fusion

2021 ◽  
pp. 1-13
Author(s):  
Shikhar Tyagi ◽  
Bhavya Chawla ◽  
Rupav Jain ◽  
Smriti Srivastava
Keyword(s):  
High Performance ◽  
Recognition Accuracy ◽  
Error Rates ◽  
Facial Features ◽  
Biometric System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Biometric Systems ◽  
Overall Performance ◽  
Recognition Systems

Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates.

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