Corresponding keypoint constrained sparse representation three‐dimensional ear recognition via one sample per person

Zhu, Qinping; Zhang, Mu; Li, Yuan

doi:10.1049/bme2.12067

Cited by 2 publications

(2 citation statements)

References 44 publications

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“…Currently, research for human auricle-related topics is mainly focused on ear detection from images [11], [12] or 3D point clouds [15], [16]. All these studies are focused on the external part of the auricle only, the detection of individual auricular landmarks and elements has not yet been studied in depth.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Auricular Point Localization for Auriculotherapy

Sun

Dong

Li³

et al. 2022

IEEE Access

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Auriculotherapy is one of the main forms of treatment in Traditional Chinese Medicine, whose potential as an alternative medicine for both health evaluation and disease treatment has been reported in many cases. However, its efficacy highly relies on the accurate localization of auricular points, which are not easy to be remembered due to their complexity. To explore an efficient way of locating auricular points, this study proposed a deep learning-based method of automatically locating auricular points from auricular images. A self-collected dataset named EID was created for TCM auriculotherapy research, with 91 auriculotherapy-related landmark points manually annotated according to the Chinese national standardization. A deep neural network structure was trained for landmark detection, and a direction normalization module was proposed to compensate for the detection error caused by the difference between the left and right ears. The trained model was validated on dataset EID. An average NME of 0.0514±0.0023 was achieved, which outperformed similar works. In addition, a certain auricular area corresponding to the digestive system was segmented based on the localized landmarks, and the results were tested in real-time video streaming. The proposed work for both auricular landmark and area identification can be widely used in auriculotherapy education and applications.

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Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Auricular Point Localization for Auriculotherapy

Sun

Dong

Li³

et al. 2022

IEEE Access

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“…Closed-set identification is not considered of operational value, because in operational biometric applications applications it can usually not be guaranteed that the probe is represented in the gallery [3]. It remains, however, relevant, because it is often used in scientific publications to quantify the recognition performance of biometric solutions, see [4], [5], [6], [7] for some recent examples.…”

Section: Introductionmentioning

confidence: 99%

On the Relation Between ROC and CMC

Veldhuis

Raja

2023

IEEE Trans. Biom. Behav. Identity Sci.

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We formulate a compact relation between the probabilistic Receiver Operating Characteristic (ROC) and the probabilistic Cumulative Match Characteristic (CMC) that predicts every entry of the probabilistic CMC as a functional on the probabilistic ROC. This result is shown to be valid for individual probabilistic ROCs and CMCs of single identities, based on the assumption that each identity has individual mated and nonmated Probabilitic Density Functions (PDF). Furthermore, it is shown that the relation still holds between the global probabilistic CMC of a gallery of identities and the average probabilistic ROC obtained by averaging the individual probabilistic ROCs of these identities involved over constant False Match Rates (FMR). We illustrate that the difference between individual probabilistic ROCs and the difference between global and average probabilistic ROCs provide an explanation for the discrepancies observed in the literature. The new formulation of the relation between probabilistic ROCs and CMCs allows us to prove that the probabilistic CMC plotted as a function of fractional rank, i.e. linearly compressed to a domain ranging from 0 to 1, will converge to the average probabilistic ROC when the gallery size increases. We illustrate our findings by experiments on synthetic and on face, fingerprint, and iris data.

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Corresponding keypoint constrained sparse representation three‐dimensional ear recognition via one sample per person

Cited by 2 publications

References 44 publications

Deep Learning-Based Auricular Point Localization for Auriculotherapy

Deep Learning-Based Auricular Point Localization for Auriculotherapy

On the Relation Between ROC and CMC

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