LMZMPM: Local Modified Zernike Moment Per-Unit Mass for Robust Human Face Recognition

Kar, Arindam; Pramanik, Sourav; Chakraborty, Arghya; Bhattacharjee, Debotosh; Ho, Edmond S. L.; Shum, Hubert P. H.

doi:10.1109/tifs.2020.3015552

Cited by 18 publications

(8 citation statements)

References 63 publications

(104 reference statements)

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“…In this section, the effectiveness of the proposed algorithm for the distortion object detection task is verified by means of experimental comparative analysis. The algorithms in this paper are compared with existing excellent object detection algorithms, such as Deep Face [ 19 ], VGG Face [ 20 ], TBE-CNN [ 21 ], DA-GAN [ 22 ], PEN-3D [ 23 ], and LMZMPM [ 24 ]. The environment in which the algorithm runs: the CPU is Intel CORE i9 10900K and the graphics card is RTX 3090 VENTUS 3X 24 G.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Glass Refraction Distortion Object Detection via Abstract Features

Cai

Chen

Sun

et al. 2022

Computational Intelligence and Neuroscience

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Glass reflection and refraction lead to missing and distorted object feature data, affecting the accuracy of object detection. In order to solve the above problems, this paper proposed a glass refraction distortion object detection via abstract features. The number of parameters of the algorithm is reduced by introducing skip connections and expansion modules with different expansion rates. The abstract feature information of the object is extracted by binary cross-entropy loss. Meanwhile, the abstract feature distance between the object domain and source domain is reduced by a loss function, which improves the accuracy of object detection under glass interference. To verify the effectiveness of the algorithm in this paper, the GRI dataset is produced and made public on GitHub. The algorithm of this paper is compared with the current state-of-the-art Deep Face, VGG Face, TBE-CNN, DA-GAN, PEN-3D, LMZMPM, and the average detection accuracy of our algorithm is 92.57% at the highest, and the number of parameters is only 5.13 M.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Glass Refraction Distortion Object Detection via Abstract Features

Cai

Chen

Sun

et al. 2022

Computational Intelligence and Neuroscience

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“…Thus, Teague [18] introduced Zernike moments (ZMs) based on orthogonal Zernike polynomials. The orthogonal ZMs have been verified to behave more robust in noisy conditions, and they can yield an almost zero value of redundancy measure [19] (Fig. 3).…”

Section: Pseudo-zernike Momentmentioning

confidence: 96%

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder

et al. 2021

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Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% ± 1.47%, a specificity of 93.13% ± 0.95%, a precision of 93.15% ± 0.89%, an accuracy of 93.23% ± 0.81%, and an F1 score of 93.23% ± 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches.

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“…The overall H@N is computed as the average value of all testing instances. Note that H@1 is equivalent to the accuracy measure used for validating traditional face recognition models where only the top result is taken into account (Kar et al 2020 ; Taigman et al. 2014 ).…”

Section: Experimental Settingmentioning

confidence: 99%

A deep feature-level fusion model for masked face identity recommendation system

Thaipisutikul

Tatiyamaneekul

Lin

et al. 2022

J Ambient Intell Human Comput

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The widespread occurrences of airborne outbreaks (e.g., COVID-19) and pollution (e.g., PM2.5) have urged people in the affected regions to protect themselves by wearing face masks. In certain areas, wearing masks amidst such health-endangering times is even enforced by law. While most people wear masks to guard themselves against airborne substances, some exploit such excuses and use face masks to conceal their identity for criminal purposes such as shoplifting, robbery, drug transport, and assault. While automatic face recognition models have been proposed, most of these models aim to identify clear, unobstructed faces for authentication purposes and cannot effectively handle cases where masks cover most facial areas. To mitigate such a problem, this paper proposes a deep-learning-based feature-fusion framework, FIREC , that combines additional demographic-estimated features such as age, gender, and race into the underlying facial representation to compensate for the information lost due to mask obstruction. Given an image of a masked face, our system recommends a ranked list of potential identities of the person behind the mask. Empirical results show that the best configuration of our proposed framework can recognize bare faces and masked faces with the accuracy of 99.34% and 97.65% in terms of Hit@10, respectively. The proposed framework could greatly benefit high-recall facial identity recognition applications such as identifying potential suspects from CCTV or passers-by’s cameras, especially during crisis times when people commonly cover their faces with protective masks.

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LMZMPM: Local Modified Zernike Moment Per-Unit Mass for Robust Human Face Recognition

Cited by 18 publications

References 63 publications

Glass Refraction Distortion Object Detection via Abstract Features

Glass Refraction Distortion Object Detection via Abstract Features

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder

A deep feature-level fusion model for masked face identity recommendation system

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