Face recognition with temporal invariance: A 3D aging model

Park, Unsang; Tong, Yiying; Jain, Anil K.

doi:10.1109/afgr.2008.4813408

Cited by 58 publications

(49 citation statements)

References 21 publications

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“…There exists a sizable amount of literature on recognition of age-separated face images, such as [24][25][26][27][28][29][30][31][32][33][34][35][36]. The existing methods can be categorized as generative or discriminative.…”

Section: Recognition and Retrieval Of Face Images Across Aging Variatmentioning

confidence: 99%

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

et al. 2018

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Demographic estimation of human face images involves estimation of age group, gender, and race, which finds many applications, such as access control, forensics, and surveillance. Demographic estimation can help in designing such algorithms which lead to better understanding of the facial aging process and face recognition. Such a study has two parts-demographic estimation and subsequent face recognition and retrieval. In this paper, first we extract facial-asymmetry-based demographic informative features to estimate the age group, gender, and race of a given face image. The demographic features are then used to recognize and retrieve face images. Comparison of the demographic estimates from a state-of-the-art algorithm and the proposed approach is also presented. Experimental results on two longitudinal face datasets, the MORPH II and FERET, show that the proposed approach can compete the existing methods to recognize face images across aging variations.

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Section: Recognition and Retrieval Of Face Images Across Aging Variatmentioning

confidence: 99%

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

et al. 2018

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“…A recent work in Biswas et al [12] studies feature drifting on face images at different ages and applies it to faceverification tasks. Other studies using age transformation for recognition include [13][14][15][16]. For age-estimation problem, Fu and Huang [17] construct a low-dimensional manifold from a set of ageseparated face images to estimate the ages of faces.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Facial Images across Age Progression by Humans

Zeng

Ling

Latecki

et al. 2012

ISRN Machine Vision

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The appearance of human faces can undergo large variations over aging progress. Analysis of facial image taken over age progression recently attracts increasing attentions in computer-vision community. Human abilities for such analysis are, however, less studied. In this paper, we conduct a thorough study of human ability on two tasks, face verification and age estimation, for facial images taken at different ages. Detailed and rigorous experimental analysis is provided, which helps understanding roles of different factors including age group, age gap, race, and gender. In addition, our study also leads to an interesting observation: for age estimation, photos from adults are more challenging than that from young people. We expect the study to provide a reference for machine-based solutions.

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“…(i) Prototype based methods [7,13,23,34,40,41] firstly divide age range into discrete age groups and compute the mean face of each age group as its prototype, and then define the differences between prototypes as axis of aging transformation. Wang et al [43] and Hill et al [15] extend this approach to PCA space and Park et al [26] apply it to 3D face data. Since some details crucial for age perception are lost in prototype computation, some other researchers specially work on rendering high resolution aging results [12].…”

Section: Previous Workmentioning

confidence: 99%