Improved performance of face recognition using CNN with constrained triplet loss layer

2017 ◽  
Author(s):  
Henry Wing Fung Yeung ◽  
Jiaxi Li ◽  
Yuk Ying Chung
Keyword(s):  
Face Recognition ◽  
Triplet Loss ◽  
Improved Performance

The identity-level angular triplet loss for cross-age face recognition

2021 ◽  
Author(s):  
Xiaoyu Chen ◽  
Henry Y. K. Lau
Keyword(s):  
Face Recognition ◽  
Triplet Loss

scholarly journals Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Hady Pranoto ◽  
Oktaria Kusumawardani
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Success Factors ◽  
Recognition System ◽  
Expression Recognition ◽  
Student Attendance ◽  
Face Recognition System ◽  
Triplet Loss ◽  
Education Process

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.

scholarly journals Enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss

2018 ◽  
Vol 79 ◽  
pp. 99-108 ◽  
Author(s):  
Daniel Sáez Trigueros ◽  
Li Meng ◽  
Margaret Hartnett
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Triplet Loss

Cross spectral, active and passive approach to face recognition for improved performance

2017 ◽  
Author(s):  
Marcin Kowalski ◽  
Artur Grudzien ◽  
Mieczyslaw Szustakowski
Keyword(s):  
Face Recognition ◽  
Improved Performance

Improved Performance and Execution Time of Face Recognition Using MRSRC

2019 ◽  
pp. 597-607
Author(s):  
Jitendra Madarkar ◽  
Poonam Sharma ◽  
Rimjhim Singh
Keyword(s):  
Face Recognition ◽  
Execution Time ◽  
Improved Performance

Eye Detection Using Color, Haar Features, and Efficient Support Vector Machine

2012 ◽  
pp. 286-309
Author(s):  
Shuo Chen ◽  
Chengjun Liu
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Detection Method ◽  
Support Vector ◽  
Grand Challenge ◽  
Detection Accuracy ◽  
Eye Detection ◽  
Color Information ◽  
Haar Features ◽  
Improved Performance

Eye detection is an important initial step in an automatic face recognition system. Though numerous eye detection methods have been proposed, many problems still exist, especially in the detection accuracy and efficiency under challenging image conditions. The authors present a novel eye detection method using color information, Haar features, and a new efficient Support Vector Machine (eSVM) in this chapter . In particular, this eye detection method consists of two stages: the eye candidate selection and validation. The selection stage picks up eye candidates over an image through color information, while the validation stage applies 2D Haar wavelet and the eSVM to detect the center of the eye among these candidates. The eSVM is defined on fewer support vectors than the standard SVM, which can achieve faster detection speed and higher or comparable detection accuracy. Experiments on Face Recognition Grand Challenge (FRGC) database show the improved performance over existing methods on both efficiency and accuracy.

scholarly journals Potential downside of high initial visual acuity

2018 ◽  
Vol 115 (44) ◽  
pp. 11333-11338 ◽  
Author(s):  
Lukas Vogelsang ◽  
Sharon Gilad-Gutnick ◽  
Evan Ehrenberg ◽  
Albert Yonas ◽  
Sidney Diamond ◽  
...  
Keyword(s):  
Face Recognition ◽  
Initial Period ◽  
Receptive Fields ◽  
Visual Development ◽  
Adaptive Function ◽  
Late Treatment ◽  
Improved Performance ◽  
And Performance ◽  
Configural Analysis ◽  
Blurred Images

Children who are treated for congenital cataracts later exhibit impairments in configural face analysis. This has been explained in terms of a critical period for the acquisition of normal face processing. Here, we consider a more parsimonious account according to which deficits in configural analysis result from the abnormally high initial retinal acuity that children treated for cataracts experience, relative to typical newborns. According to this proposal, the initial period of low retinal acuity characteristic of normal visual development induces extended spatial processing in the cortex that is important for configural face judgments. As a computational test of this hypothesis, we examined the effects of training with high-resolution or blurred images, and staged combinations, on the receptive fields and performance of a convolutional neural network. The results show that commencing training with blurred images creates receptive fields that integrate information across larger image areas and leads to improved performance and better generalization across a range of resolutions. These findings offer an explanation for the observed face recognition impairments after late treatment of congenital blindness, suggest an adaptive function for the acuity trajectory in normal development, and provide a scheme for improving the performance of computational face recognition systems.

scholarly journals Self-restrained Triplet Loss for Accurate Masked Face Recognition

2021 ◽  
pp. 108473
Author(s):  
Fadi Boutros ◽  
Naser Damer ◽  
Florian Kirchbuchner ◽  
Arjan Kuijper
Keyword(s):  
Face Recognition ◽  
Triplet Loss

scholarly journals Deep Attribute Guided Representation for Heterogeneous Face Recognition

2018 ◽  
Author(s):  
Decheng Liu ◽  
Nannan Wang ◽  
Chunlei Peng ◽  
Jie Li ◽  
Xinbo Gao
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Convolutional Networks ◽  
Common Space ◽  
Face Images ◽  
Heterogeneous Face Recognition ◽  
Triplet Loss ◽  
High Level ◽  
Different Sources ◽  
High Level Feature

Heterogeneous face recognition (HFR) is a challenging problem in face recognition, subject to large texture and spatial structure differences of face images. Different from conventional face recognition in homogeneous environments, there exist many face images taken from different sources (including different sensors or different mechanisms) in reality. Motivated by human cognitive mechanism, we naturally utilize the explicit invariant semantic information (face attributes) to help address the gap of different modalities. Existing related face recognition methods mostly regard attributes as the high level feature integrated with other engineering features enhancing recognition performance, ignoring the inherent relationship between face attributes and identities. In this paper, we propose a novel deep attribute guided representation based heterogeneous face recognition method (DAG-HFR) without labeling attributes manually. Deep convolutional networks are employed to directly map face images in heterogeneous scenarios to a compact common space where distances mean similarities of pairs. An attribute guided triplet loss (AGTL) is designed to train an end-to-end HFR network which could effectively eliminate defects of incorrectly detected attributes. Extensive experiments on multiple heterogeneous scenarios (composite sketches, resident ID cards) demonstrate that the proposed method achieves superior performances compared with state-of-the-art methods.

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