Preliminary Studies on a Large Face Database

Yip, Benjamin; Bingham, Garrett; Kempfert, Katherine C.; Fabish, Jonathan; Kling, Troy; Chen, Cuixian; Wang, Yishi

doi:10.1109/bigdata.2018.8622432

Cited by 5 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, Fg-Net contains subjects from one caucasian race, whereas Morph dataset contains the caucasoid, negroid, and mongoloid races [17]. Furthermore, the total images (samples) in Fg-Net are 1002 with 82 subjects, while that of Morph is 55,134 with 13,658 subjects [18]- [22], while details of both datasets are as shown in Table 2 and Table 3 and Table 4 depicts the Morph numbers of facial image and decade-of-life. However, the Similarity is that both datasets contain face images of the same subjects at various age gaps.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of augmented datasets performances of age invariant face recognition models

et al. 2021

View full text Add to dashboard Cite

The popularity of face recognition systems has increased due to their non-invasive method of image acquisition, thus boasting the widespread applications. Face ageing is one major factor that influences the performance of face recognition algorithms. In this study, the authors present a comparative study of the two most accepted and experimented face ageing datasets (FG-Net and morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. Four types of noises were added to the two face ageing datasets at the preprocessing stage. The addition of noise at the preprocessing stage served as a data augmentation technique that increased the number of sample images available for deep convolutional neural network (DCNN) experimentation, improved the proposed AIFR model and the trait aging features extraction process. The proposed AIFR models are developed with the pre-trained Inception-ResNet-v2 deep convolutional neural network architecture. On testing and comparing the models, the results revealed that FG-Net is more efficient over Morph with an accuracy of 0.15%, loss function of 71%, mean square error (MSE) of 39% and mean absolute error (MAE) of -0.63%.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparative analysis of augmented datasets performances of age invariant face recognition models

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The Morph II dataset also records other information of each subject which includes gender, race, and whether the subject is wearing glasses. Literature [29], [40] shows that the Morph II dataset has some mislabelled data and given the filtering approach which could be used for our experimental works.…”

Section: Morphmentioning

confidence: 99%

A Study on the Effect of Ageing in Facial Authentication and the Utility of Data Augmentation to Reduce Performance Bias Across Age Groups

Yao,

Farooq,

Lemley

et al. 2023

IEEE Access

View full text Add to dashboard Cite

This work presents a study on the effects of aging on the performance and reliability of facial authentication methods. First, a brief review of the literature on the effect of age on face recognition algorithms is presented, followed by a detailed description of the face aging datasets. In contrast with some recent studies, we demonstrate significant variations in authentication robustness between age groups. The second part of this paper focuses on a comprehensive comparative assessment on the effects across age groups. Four different face recognition algorithms are studied of which three are state-of-the-art neural network based models and the fourth one is a conventional machine learning model. Two different age range threshold settings (±3 in Experiment Category A and ±5 in Experiment Category B) of the age groups are adopted in the experimental analysis to get a proper comparison. Moreover, a synthetic aging method has been incorporated to augment the age data. Experimental result shows that the older adults group are easier to identify with higher levels of accuracy and robustness compared to other age groups, while younger adults are the most challenging and false authentications are more likely to occur.

show abstract

Multimodality for Reliable Single Image Based Face Morphing Attack Detection

Ramachandra

Li²

2022

IEEE Access

View full text Add to dashboard Cite

Face morphing attacks have demonstrated a high vulnerability on human observers and commercial off-the-shelf Face Recognition Systems (FRS), especially in the border control scenario. Therefore, detecting face morphing attacks is paramount to achieving a reliable and secure border control operation. This work presents a novel framework for the Single image-based Morphing Attack Detection (S-MAD) based on the multimodal regions such as eyes, nose, and mouth. Each of these regions is processed using colour scale-space representation on which two different types of features are extracted using Binarised Statistical Image Features (BSIF) and Local Binary Features (LBP) techniques. These features are then fed to the classifiers such as Probabilistic Collaborative Representation Classifier (P-CRC) and Spectral Regression Kernel Discriminant Analysis (SRKDA). Their decisions are combined at score level to make the final decision. Extensive experiments are carried out on three different face morphing datasets to benchmark the performance of the proposed method with the existing methods. Further, the proposed method is benchmarked on the Bologna Online Evaluation Platform (BOEP). Obtained results demonstrate the improved performance of the proposed method over existing state-of-the-art methods.

show abstract

Preliminary Studies on a Large Face Database

Cited by 5 publications

References 17 publications

Comparative analysis of augmented datasets performances of age invariant face recognition models

Comparative analysis of augmented datasets performances of age invariant face recognition models

A Study on the Effect of Ageing in Facial Authentication and the Utility of Data Augmentation to Reduce Performance Bias Across Age Groups

Multimodality for Reliable Single Image Based Face Morphing Attack Detection

Contact Info

Product

Resources

About