Age-invariant face recognition based on identity inference from appearance age

Zhou, Huiling; Lam, Kin‐Man

doi:10.1016/j.patcog.2017.10.036

Cited by 42 publications

(21 citation statements)

References 44 publications

(75 reference statements)

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“…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

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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%.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Zhou et al . recently used an AIFR identity inference model based on linear probabilistic analytics and the EM algorithm [43].…”

Section: IImentioning

confidence: 99%

Analysis of Age Invariant Face Recognition using QSVM-PCA

Dhamija¹,

Dubey²

2020

Preprint

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Forage, face recognition is one of the most demanding field challenges, since aging affects the shape and structure of the face. Age invariant face recognition (AIFR) is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The AIFR, however, is still evolving and evolving, providing substantial potential for further study and progress inaccuracy. Major issues with the AIFR involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the AIFR systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVM-PCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging datasets of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%.

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“…In 2018, Zhou et al [2], introduced a model called Identity-Inference, which depend on an age subspace learnt from appearance-age labels. In the first step, they used Probabilistic Linear Discriminant Analysis (PLDA).…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the progression of age during the life cannot model by using an easy progression. For instance, people who are in a healthy case could reach longevity will likely look fairly different from other people who could be suffered from incidents or illness in their lives [2].…”

Section: Introductionmentioning

confidence: 99%

The Effect of FaceAgeing in Face Recogntion

Elmahmudi¹,

Ugail²

2019

EasyChair Preprints

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Age progression still has many challenges and affects face recognition tasks. Over the past decade, many researchers have been working on face processing system in order to tackle these challenges of face recognition gap especially in the presence of variation in the age of an individual. In this paper, we propose a new preprocessing method that can improve a face for recognition. Here we align the faces into one template and remove a background which can hold some objects that affect the recognition. In particular, in the stage of recognition, we use a convolutional neural network based architecture along with the pre-trained VGG-Face model to extract features. These features are passed into a Cosine similarity classifier classification. A thorough experimental examination of face identification is achieved on the most widely used age database which is FGNET aging with a huge age gap and different ethnicity and gender. Our experimental results on the FGNET face database illustrate that the proposed approach has the ability to outperform the present state of the art methods.

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Age-invariant face recognition based on identity inference from appearance age

Cited by 42 publications

References 44 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

Analysis of Age Invariant Face Recognition using QSVM-PCA

The Effect of FaceAgeing in Face Recogntion

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