Computational methods for modeling facial aging: A survey

Ramanathan, N.; Chellappa, Rama; Biswas, Soma

doi:10.1016/j.jvlc.2009.01.011

Cited by 164 publications

(92 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Age variation in face recognition can be handled in a manner similar to illumination and pose variations by using novel appearance synthesis or discriminative feature extraction [6]. For example, synthesis methods can eliminate age gaps between face images by synthesizing facial appearances via learning the face aging process [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

How Does Aging Affect Facial Components?

Otto

Huang

Jain

2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. There is growing interest in achieving age invariant face recognition due to its wide applications in law enforcement. The challenge lies in that face aging is quite a complicated process, which involves both intrinsic and extrinsic factors. Face aging also influences individual facial components (such as the mouth, eyes, and nose) differently. We propose a component based method for age invariant face recognition. Facial components are automatically localized based on landmarks detected using an Active Shape Model. Multi-scale local binary pattern and scale-invariant feature transform features are then extracted from each component, followed by random subspace linear discriminant analysis for classification. With a component based representation, we study how aging influences individual facial components on two large aging databases (MORPH Album2 and PCSO). Per component performance analysis shows that the nose is the most stable component during face aging. Age invariant recognition exploiting demographics shows that face aging has more influence on females than males. Overall, recognition performance on the two databases shows that the proposed component based approach is more robust to large time lapses than FaceVACS, a leading commercial face matcher.

show abstract

Section: Introductionmentioning

confidence: 99%

How Does Aging Affect Facial Components?

Otto

Huang

Jain

2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The MORPH dataset has facilitated studies on synthetic aging [16,22], age invariant face recognition [11,12,16], age estimation [5], and aging analysis [17]. A broader examination of facial aging methods in the literature can be found in the summary provided by Ramanathan et al [19].…”

Section: Introductionmentioning

confidence: 99%

Face recognition across time lapse: On learning feature subspaces

Klare

Jain

2011

2011 International Joint Conference on Biometrics (IJCB)

View full text Add to dashboard Cite

There is a growing interest in understanding the impact of aging on face recognition performance, as well as designing recognition algorithms that are mostly invariant to temporal changes. While some success has been made on this front, a fundamental questions has yet to be answered: do face recognition systems that compensate for the effects of aging compromise recognition performance for faces that have not undergone any aging? The studies in this paper help confirm that age invariant systems do seem to decrease performance in non-aging scenarios. This is demonstrated by performing training experiments on the largest face aging dataset studied in the literature to date (over 200,000 images from roughly 64,000 subjects). Further experiments conducted in this research help demonstrate the impact of aging on two leading commercial face recognition systems. We also determine the regions of the face that remain the most stable over time.

show abstract

“…The aim of this review is not to present an exhaustive literature review of the topic but rather to highlight the evolution of the topic. Ramanathan et al [20] present a more detailed presentation of the related literature…”

Section: Literature Reviewmentioning

confidence: 99%

Human Age Group Prediction from Unknown Facial Image

Arumugam¹,

Muthukumar²,

S³

et al. 2017

IJARCSSE

View full text Add to dashboard Cite

Abstract-In today's internet era, Age Specific Human-Computer Interaction (ASHCI) is widely demanded by numerous applications in daily life for smart communication over the digital communication devices. This helps to automatically choose the vocabulary, interface and services that suit the customer's age. More cleverly, the web browser can determine by itself, whether the user satisfies the age limitation to view certain web pages and also vending machine will refuse to sell alcohol or cigarettes to the under-age people. This paper uses the multiple facial features from the mug-shot images for classifying them into appropriate age group.

show abstract

Computational methods for modeling facial aging: A survey

Cited by 164 publications

References 29 publications

How Does Aging Affect Facial Components?

How Does Aging Affect Facial Components?

Face recognition across time lapse: On learning feature subspaces

Human Age Group Prediction from Unknown Facial Image

Contact Info

Product

Resources

About