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- W3201810669 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." @default.
- W3201810669 created "2021-10-11" @default.
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- W3201810669 date "2021-01-01" @default.
- W3201810669 modified "2023-10-18" @default.
- W3201810669 title "Deep Component Based Age Invariant Face Recognition in an Unconstrained Environment" @default.
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- W3201810669 doi "https://doi.org/10.1007/978-3-030-88113-9_8" @default.
- W3201810669 hasPublicationYear "2021" @default.
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