Proceedings ArticleDOI
Bacteria Foraging Fusion for Face Recognition across Age Progression
Daksha Yadav,Mayank Vatsa,Richa Singh,Massimo Tistarelli +3 more
- pp 173-179
TLDR
The proposed algorithm mitigates the effect of facial changes caused due to aging by combining the LBP features of global and local facial regions at match score level, by means of the bacteria foraging fusion algorithm.Abstract:
The phenomenon of "aging" in humans leads to significant variations in facial features. Various factors such as bone growth, ethnicity and dietary habits influence the facial aging pattern. This increases the difficulty in performing automated face recognition. In this paper, we propose an algorithm that improves the performance of face recognition by applying the bacteria foraging fusion algorithm. The proposed algorithm mitigates the effect of facial changes caused due to aging by combining the LBP features of global and local facial regions at match score level, by means of the bacteria foraging fusion algorithm. Experimental results are presented using the FG-Net and IIITDelhi face aging databases. The IIITDelhi database, which has been collected by the authors, consists of over 2600 age-separated labeled face images of 102 individuals. To account for real life and natural conditions, images include changes in the face due to illumination, pose, and presence of accessories such as eyeglasses. The results demonstrate that the proposed approach outperforms traditional fusion schemes, existing algorithms and a commercial system.read more
Citations
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Journal ArticleDOI
Overview of research on facial ageing using the FG-NET ageing database
TL;DR: The ultimate aims of this study are to present concrete facts related to research activities in facial ageing during the past decade, provide an indication of the main methodologies adopted, present a comprehensive list of benchmark results and most importantly provide roadmaps for future trends, requirements and research directions in Facial ageing.
Posted Content
Personalized Age Progression with Aging Dictionary
TL;DR: In this paper, a set of age-group specific dictionaries are learned, where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups, and a linear combination of these patterns expresses a particular personalized aging process.
Proceedings ArticleDOI
Personalized Age Progression with Aging Dictionary
TL;DR: The advantages of the proposed solution over other state-of-the-arts in term of personalized aging progression, as well as the performance gain for cross-age face verification by synthesizing aging faces are demonstrated.
Journal ArticleDOI
On soft biometrics
Mark S. Nixon,Paulo Lobato Correia,Kamal Nasrollahi,Thomas B. Moeslund,Abdenour Hadid,Massimo Tistarelli +5 more
TL;DR: The achievements that have been made in recognition by and in estimation of these parameters are surveyed, describing how these approaches can be used and where they might lead to.
Journal ArticleDOI
Personalized Age Progression with Bi-Level Aging Dictionary Learning
TL;DR: This work aims to automatically render aging faces in a personalized way by proposing a novel Bi-level Dictionary Learning based Personalized Age Progression (BDL-PAP) method, formulated to learn the aging dictionaries based on face pairs from neighboring age groups.
References
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