2022
DOI: 10.1007/s11042-022-12382-5
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Face recognition in a large dataset using a hierarchical classifier

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Cited by 3 publications
(2 citation statements)
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“…The proposed method involves using a neural network with multiple layers to analyze facial images and learn expression characteristics. The approach was tested on the FERET database, which did not include masks, and the results show that it achieves better recognition accuracy than other methods [14]. Furthermore, deep learning has succeeded in single-domain datasets, and current research combines multimodal inputs.…”
Section: Introductionmentioning
confidence: 99%
“…The proposed method involves using a neural network with multiple layers to analyze facial images and learn expression characteristics. The approach was tested on the FERET database, which did not include masks, and the results show that it achieves better recognition accuracy than other methods [14]. Furthermore, deep learning has succeeded in single-domain datasets, and current research combines multimodal inputs.…”
Section: Introductionmentioning
confidence: 99%
“…Especially in a large database, the face recognition model errs in distinguishing features. Previous works have used various methods like clustering [26] and hashing [27] to increase face recognition accuracy in uncontrolled conditions. These methods work well on a large dataset problem and the angularity of faces.…”
Section: Introductionmentioning
confidence: 99%