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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

Deep Component Based Age Invariant Face Recognition in an Unconstrained Environment

verfasst von : Amad Asif, Muhammad Atif Tahir, Mohsin Ali

Erschienen in: Advances in Computational Collective Intelligence

Verlag: Springer International Publishing

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Abstract

Age Invariant face recognition is one of the challenging problems in pattern recognition. Most existing face recognition algorithms perform well under controlled conditions where the data set is collected with careful cooperation with the individuals. However, in most real-world applications, the user usually has little or no control over environmental conditions. This paper proposes efficient deep component-based age-invariant face recognition algorithm in an unconstrained environment. The algorithm detects face from an image, align the face and extract the facial components (eye, mouth and nose). Each facial component is then trained using deep neural network. Thus, deep features are extracted from each component. Support vector machine is then used in classification stage. Experiments are conducted on two challenging benchmarks: AgeDb30 and Pins-Face. Results have shown significant improvement when compared with the state of the art baseline approach.

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Vorheriges Kapitel Hybrid Biogeography-Based Optimization Algorithm for Job Shop Scheduling Problem with Time Lags and Single Transport Robot
Nächstes Kapitel Hybrid Vision Transformer for Domain Adaptable Person Re-identification
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Metadaten
Titel
Deep Component Based Age Invariant Face Recognition in an Unconstrained Environment
verfasst von
Amad Asif
Muhammad Atif Tahir
Mohsin Ali
Copyright-Jahr
2021
Buch
Advances in Computational Collective Intelligence

Print ISBN: 978-3-030-88112-2

Electronic ISBN: 978-3-030-88113-9

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-88113-9_8

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