Age-invariant face recognition using gender specific 3D aging modeling

Riaz, Sidra; Ali, Zahid; Park, Unsang; Choi, Jongmoo; Masi, Iacopo; Natarajan, Prem

doi:10.1007/s11042-019-7694-1

Cited by 8 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accuracy is derived from the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values as shown in (1). True positives are correct positive classifications.…”

Section: Accuracymentioning

confidence: 99%

“…The augmented dataset is usually used independently of each other to verify the invariability of designed face recognition systems. Two of the most common face image datasets used in age-invariant face recognition, FG-NET and MORPH [1] are usually at the centre of comparisons made to check the performance of age-invariant face recognition system. The goal is to have a good performance for Bulletin of Electr Eng & Inf ISSN: 2302-9285  Comparative analysis of augmented datasets performances of age invariant face… (Kennedy Okokpujie) 1357 all datasets used for face recognition.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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: Accuracymentioning

confidence: 99%

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 main factors that affect FR include (i) pose [37,47,3,18], (ii) illumination [2,49,42], (iii) facial expression [29,30,28], (iv) age [31,6] and, (v) gender variations [44,27].…”

Section: Introductionmentioning

confidence: 99%

Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects

Varkarakis,

Yao,

Corcoran

2021

Preprint

View full text Add to dashboard Cite

The recent availability of low-power neural accelerator hardware, combined with improvements in end-to-end neural facial recognition algorithms provides enabling technology for on-device facial authentication. The present research work examines the effects of directional lighting on a State-of-Art (SoA) neural face recognizer. A synthetic re-lighting technique is used to augment data samples due to the lack of public data-sets with sufficient directional lighting variations. Top lighting and its variants (top-left, topright) are found to have minimal effect on accuracy, while bottom-left or bottom-right directional lighting have the most pronounced effects. Following the fine-tuning of network weights, the face recognition model is shown to achieve close to the original Receiver Operating Characteristic curve (ROC) performance across all lighting conditions, and demonstrates an ability to generalize beyond the lighting augmentations used in the fine-tuning dataset. This work shows that a SoA neural face recognition models can be tuned to compensate for directional lighting effects, removing the need for a pre-processing step prior to applying facial recognition.

show abstract

“…Jia et al [33] proposed the use of deep autoencoder for compact shape feature expression and image retrieval, and achieved good results. Riaz et al [34] compared and analyzed the performance of auto-encoder and principal component analysis in face recognition applications. Both methods are used for feature generation and selection, and it is found that auto-encoder is superior to principal component analysis.…”

Section: Introductionmentioning

confidence: 99%

Image Target Recognition Model of Multi- Channel Structure Convolutional Neural Network Training Automatic Encoder

et al. 2020

View full text Add to dashboard Cite

The self-encoder is a typical unsupervised deep learning algorithm. In the field of unsupervised learning, it is very popular with researchers. Therefore, in view of the shortage of labeled training samples, the convolution kernel of a typical convolutional neural network is set by experience, and the network structure is fixed and it is difficult to re-learn later. This paper combines the convolutional neural network and the automatic encoder, and proposes a multi-based the method of integrated network structure to extract the features of the image for recognition. First, the SAE pre-trained CNN model convolution kernel is used to pre-train based on the classic CNN structure. Secondly, input and process image data of different scales to extract image space and spectral features respectively. Then, construct multiple channels, and use different scale filters and sampling intervals for different channels. Finally, after one layer of down sampling, the feature maps obtained from multiple channels are input into the fully connected layer, and after a hidden layer, the features finally used for classification are obtained. Experimental results show that the proposed method uses sparse automatic coding for pre-training time efficiency increased by 50%, and can further improve the recognition accuracy, the highest recognition rate reached 0.985. INDEX TERMS Convolutional neural network; multi-channel; training; automatic encoder; recognition.

show abstract

Age-invariant face recognition using gender specific 3D aging modeling

Cited by 8 publications

References 15 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

Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects

Image Target Recognition Model of Multi- Channel Structure Convolutional Neural Network Training Automatic Encoder

Contact Info

Product

Resources

About