Periocular recognition via effective textural descriptor

Kumar, Kalagara Chaitanya; Lala, Aryan; Vyas, Ritesh; Sharma, Manoj

doi:10.1109/upcon50219.2020.9376575

Cited by 4 publications

(8 citation statements)

References 32 publications

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“…A previous study in [15] utilized the discrete wavelet transform (DWT) and combined it with directionally threshold LBP. The proposed method was evaluated on 1.200 periocular images with left and right periocular regions.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…This work was observed using three orthogonal wavelets with smaller support, i.e., Haar, Daubechies, and Coiflet wavelets. Although the recognition system was successfully built, the works in [15] did not evaluate the complete family of orthogonal and biorthogonal wavelets and did not compare the results against the characteristics of the wavelet.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…1 shows the WPT for two levels (J-1 and J-2). With a tree-like structure, where each parent node produces two children for one-dimensional data (binary tree) and four children for two-dimensional data (quad-tree), two levels of quad-tree WPT produce a total of 21 wavelet coefficients (nodes) from I (J,0) to I (J- 2,15) . With the same level of decomposition, the DWT only produces a total of 9 wavelet sub-bands, as shown in Fig.…”

Section: Related Theory a Wavelet Packet Transform And Best Basis Sel...mentioning

confidence: 99%

“…In the second investigation, we observe the effect of the three periocular regions, eyes regions, eyebrows regions, and both eyes and eyebrows regions. Previous works have often investigated the left and right regions of the periocular [15] and the region of interest (ROI) cropped from the periocular regions [60]. The works in [11], [16], [61], and [62] have also investigated the importance of eyebrows in periocular recognition.…”

Section: ) Analysis Of Three Periocular Regionsmentioning

confidence: 99%

“…The dimensional reduction-based such as principal component analysis (PCA) [12] and linear discriminant analysis (LDA) [13] have been evaluated. Multi-resolution analysis methods, such as wavelet-based [14], [15], and scale-space [16], have been employed to evaluate periocular regions. The periocular recognition system has also been tested using feature descriptor methods such as scale-invariant feature transform (SIFT), speeded up robust features (SURF), binary robust invariant scalable keypoints (BRISK), oriented FAST, and rotated BRIEF (ORB) [17], [18], deep learning and neural network-based [19], [20].…”

Section: Introductionmentioning

confidence: 99%

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Curvature Best Basis: A Novel Criterion to Dynamically Select a Single Best Basis as the Extracted Feature for Periocular Recognition

et al. 2022

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Aiming at the problems in the best basis selection, this paper presents a novel criterion based on the statistical measurement of the curvature wavelet coefficient to dynamically select the single best basis of the quad-tree wavelet packet transformation. The selected single best basis works as an extracted feature for biometric periocular recognition system. The proposed method first extracts the mean curvature of wavelet coefficients inside the quad-tree wavelet packet transform. Second, the method finds the most distinctive features based on the largest standard deviation and dynamically selects the extracted curvature wavelet coefficients as the single best basis. Third, the selected single curvature best basis works as an extracted feature, and then it is combined with the histogram of oriented gradients method. Finally, the support vector machine is employed to perform classification. Two datasets of two-dimensional periocular digital images are tested against the proposed method. To show the extended ability, we analyze the curvature best basis method against wavelet functions and characteristics and test the proposed method against the plain face and masked face recognition. The proposed method achieves the highest performance results inside periocular recognition (97.53% accuracy for UBIPr-1 and 97.77% accuracy for EYB-P1), masked face recognition (98.11% accuracy), and plain face recognition (98.26% accuracy). The proposed method is robust against glasses occlusion, artificial geometry transformations, Gaussian and salt pepper noise. Comparison with other works in a similar recognition system shows that our proposed curvature best basis method yields the highest performance results.INDEX TERMS Best basis, curvature, periocular recognition, wavelet packet transform.

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Section: A Literature Reviewmentioning

confidence: 99%

Section: A Literature Reviewmentioning

confidence: 99%

Section: Related Theory a Wavelet Packet Transform And Best Basis Sel...mentioning

confidence: 99%