Label Distribution Learning Based Age-Invariant Face Recognition

Huang, Hai; Song, Cheng; Zhong, Hong; Xu, Liutong

doi:10.1007/978-3-030-26142-9_19

Cited by 2 publications

(3 citation statements)

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“…And k‐nearest neighbour and support vector machine (SVM) used for classification. Huang et al (2019) designed a new deep learning approach by using Siamese neural network trained with a label distribution loss function that attempts to bring class conditional probability distributions closer to each other. Deng et al (2017), used the marginal loss to enhance the discriminative power of the deeply learned features.…”

Section: Introductionmentioning

confidence: 99%

“…Neural network (NN) based methodsWith the advent of fast computing machines, face recognition field has also explored and experimented with deep neural networks. Various studies showMoustafa et al (2020), andHuang et al (2019), the deep neural networks use discriminative methods in the area of the face aging process also Moustafa et al (2020). used a pre-trained VGG-face model to extract compact face features.…”

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confidence: 99%

“…They fused the features by using the real-time feature-level multi-discriminant correlation analysis. And k-nearest neighbour and support vector machine (SVM) used for classification Huang et al (2019). designed a new deep learning approach by using Siamese neural network trained with a label distribution loss function that attempts to bring class conditional probability distributions closer to each other Deng et al (2017),.…”

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Age invariant face recognition using discriminative tensor subspace learning with fuzzy based classification

Mittal

Patel

2023

Expert Systems

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Aging is a complex process that affects both the shape and texture of a face. In the aging domain, various methods have been proposed for age estimation or simulation of the face based on an image at a given age. Face recognition methods that are designed for automatic face recognition are still a challenging issue. We present a novel age invariant face recognition method using tensor subspace learning with fuzzy synthetic classification. Local Binary Pattern (LBP) processed face images are used to learn the tensor sub-space, which converts high dimensional feature space to age-invariants while retaining key elements of the local geometrical structure of the face. Tensor sub-space is better in terms of sub-space learning and feature extraction than Principal Component Analysis (PCA)/Linear Discriminant Analysis (LDA) that been used in many techniques in the literature. Tensor Normalized Face images are generated which exhibit maximum inter-class distances and minimum intra-class distances. Further local patches are used for synthesizing global Fuzzy membership scores to classify the test face images. Experiments performed on standard faceaging datasets, namely FG-NET, AGEDB, and MORPH-Album-II, and received accuracy of 99.15%, 99.20%, and 99.8%, respectively. Experimental results outperform the current state-of-the-art techniques, and results show the promise of the proposed system for personal identification despite the aging process. It also proved that the local descriptor gives better performance over the global descriptor like PCA for the aging process. The method also demonstrated improved performance as compared with compute-intensive methods that required training on deep networks.

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