Improved Face Recognition With Fractal-Based Texture Analysis

Improved Face Recognition With Fractal-Based Texture Analysis

Rajalaxmi Padhy, Aishwarya Dash, Sanjit Kumar Dash, Jibitesh Mishra
Copyright: © 2021 |Pages: 13
DOI: 10.4018/IJCVIP.2021070103
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Abstract

Fractals are useful to uniquely represent texture in the human face, which serves as an equivalent of human vision. FaceNet, calculating face descriptors of a person, has been observed to perform with setbacks when several factors of occlusion are present. This paper proposes a new methodology that exploits the self-similar patterns in a person's face to highlight and enhance regions of high texture in a facial image. The system maps the original image into a representation in the pre-processing stage of computer vision. This representation when fed as an input to the FaceNet CNN optimizes the face embedding generated. An SVM classifier separates the hard positive examples from the hard negative examples during classification. The model is trained using YouTube Faces DB as primary dataset and for validation; a custom dataset is designed to verify a person's identity despite the presence of secondary factors such as expressions and forgery. The proposed model attained an overall accuracy of 96.73% with the YouTube Faces DB, and a notable reduction in the false positive rates is observed.
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In this section, the different groups of texture classification methods are discussed. on the later parts of this section, we explain the fractal geometry concepts and also give a brief view on the existing work on facial recognition, especially the ones that use Inception models.

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