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Open AccessJournal ArticleDOI

On Recognizing Face Images With Weight and Age Variations

TLDR
The proposed algorithm utilizes neural network and random decision forest to encode age variations across different weight categories to improve the performance of face recognition with age variations.
Abstract
With the increase in age, there are changes in skeletal structure, muscle mass, and body fat. For recognizing faces with age variations, researchers have generally focused on the skeletal structure and muscle mass. However, the effect of change in body fat has not been studied with respect to face recognition. In this paper, we incorporate weight information to improve the performance of face recognition with age variations. The proposed algorithm utilizes neural network and random decision forest to encode age variations across different weight categories. The results are reported on the WhoIsIt database prepared by the authors containing 1109 images from 110 individuals with age and weight variations. The comparison with existing state-of-the-art algorithms and commercial system on WhoIsIt and FG-Net databases shows that the proposed algorithm outperforms existing algorithms significantly.

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Citations
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Journal ArticleDOI

A survey on deep learning based face recognition

TL;DR: Major deep learning concepts pertinent to face image analysis and face recognition are reviewed, and a concise overview of studies on specific face recognition problems is provided, such as handling variations in pose, age, illumination, expression, and heterogeneous face matching.
Journal ArticleDOI

Improving cross-resolution face matching using ensemble-based co-transfer learning.

TL;DR: A co-transfer learning framework is proposed, which is a cross-pollination of transfer learning and co-training paradigms and is applied for cross-resolution face matching and enhances the performance of cross- resolution face recognition.
Proceedings ArticleDOI

MDLFace: Memorability augmented deep learning for video face recognition

TL;DR: A memorability based frame selection algorithm is presented that enables automatic selection of memorable frames for facial feature extraction and matching and achieves state-of-the-art performance at low false accept rates.
Journal ArticleDOI

Age invariant face recognition: a survey on facial aging databases, techniques and effect of aging

TL;DR: A survey of techniques, effects of aging on performance analysis and facial aging databases, and discussions on the findings, conclusions and future directions for new researchers are presented.
Journal ArticleDOI

Recent Advances in Deep Learning Techniques for Face Recognition

TL;DR: In this paper, the authors present a comprehensive analysis of various face recognition (FR) systems that leverage the different types of DL techniques, and for the study, they summarize 171 recent contributions from this area and discuss improvement ideas, current and future trends of FR tasks.
References
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Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI

Robust Real-Time Face Detection

TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Journal ArticleDOI

A comparative study of texture measures with classification based on featured distributions

TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.
Proceedings ArticleDOI

Random decision forests

TL;DR: In this article, the authors proposed a method to construct tree-based classifiers whose capacity can be arbitrarily expanded for increases in accuracy for both training and unseen data, which can be monotonically improved by building multiple trees in different subspaces of the feature space.
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