CCFace: Classification Consistency for Low-Resolution Face Recognition

In recent years, deep face recognition methods have demonstrated impressive results on in-the-wild datasets. However, these methods have shown a significant decline in performance when applied to real-world low-resolution benchmarks like TinyFace or SCFace. To address this challenge, we propose a novel classification consistency knowledge distillation approach that transfers the learned classifier from a high-resolution model to a low-resolution network. This approach helps in finding discriminative representations for low-resolution instances. To further improve the performance, we designed a knowledge distillation loss using the adaptive angular penalty inspired by the success of the popular angular margin loss function. The adaptive penalty reduces overfitting on low-resolution samples and alleviates the convergence issue of the model integrated with data augmentation. Additionally, we utilize an asymmetric cross-resolution learning approach based on the state-of-the-art semi-supervised representation learning paradigm to improve discriminability on low-resolution instances and prevent them from forming a cluster. Our proposed method outperforms state-of-the-art approaches on low-resolution benchmarks, with a three percent improvement on TinyFace while maintaining performance on high-resolution benchmarks.Comment: 2023 IEEE International Joint Conference on Biometrics (IJCB

Deep Boosting Multi-Modal Ensemble Face Recognition with Sample-Level Weighting

Deep convolutional neural networks have achieved remarkable success in face recognition (FR), partly due to the abundant data availability. However, the current training benchmarks exhibit an imbalanced quality distribution; most images are of high quality. This poses issues for generalization on hard samples since they are underrepresented during training. In this work, we employ the multi-model boosting technique to deal with this issue. Inspired by the well-known AdaBoost, we propose a sample-level weighting approach to incorporate the importance of different samples into the FR loss. Individual models of the proposed framework are experts at distinct levels of sample hardness. Therefore, the combination of models leads to a robust feature extractor without losing the discriminability on the easy samples. Also, for incorporating the sample hardness into the training criterion, we analytically show the effect of sample mining on the important aspects of current angular margin loss functions, i.e., margin and scale. The proposed method shows superior performance in comparison with the state-of-the-art algorithms in extensive experiments on the CFP-FP, LFW, CPLFW, CALFW, AgeDB, TinyFace, IJB-B, and IJB-C evaluation datasets.Comment: 2023 IEEE International Joint Conference on Biometrics (IJCB

Information Maximization for Extreme Pose Face Recognition

In this paper, we seek to draw connections between the frontal and profile face images in an abstract embedding space. We exploit this connection using a coupled-encoder network to project frontal/profile face images into a common latent embedding space. The proposed model forces the similarity of representations in the embedding space by maximizing the mutual information between two views of the face. The proposed coupled-encoder benefits from three contributions for matching faces with extreme pose disparities. First, we leverage our pose-aware contrastive learning to maximize the mutual information between frontal and profile representations of identities. Second, a memory buffer, which consists of latent representations accumulated over past iterations, is integrated into the model so it can refer to relatively much more instances than the mini-batch size. Third, a novel pose-aware adversarial domain adaptation method forces the model to learn an asymmetric mapping from profile to frontal representation. In our framework, the coupled-encoder learns to enlarge the margin between the distribution of genuine and imposter faces, which results in high mutual information between different views of the same identity. The effectiveness of the proposed model is investigated through extensive experiments, evaluations, and ablation studies on four benchmark datasets, and comparison with the compelling state-of-the-art algorithms.Comment: INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS (IJCB 2022

Synthesis and characterization of Sm2(MoO4)3, Sm2(MoO4)3/GO and Sm2(MoO4)3/C3N4 nanostructures for improved photocatalytic performance and their anti-cancer the MCF-7 cells

Samarium molybdate nanoparticles (Sm2(MoO4)3) were prepared through a hydrothermal procedure and were used to form various composites with graphene oxide (GO) and carbon nitride (C3N4). The changes in the dimensions and morphology of the products were prepared using template agents like cetyltrimethyl ammonium bromide (CTAB), Sodium dodecyl sulfate (SDS) (�90), Triton X-100 (90), Polyvinyl alcohol (95), Ethylene glycol (�99), and polyvinylpyrrolidone (PVP). DRS analysis indicated band gap for the Sm2(MoO4), Sm2(MoO4)3/GO, and Sm2(MoO4)3/C3N4 as 3.75, 3.15, and 3.4 respectively. The characteristics of the prepared nanostructures were studied through X-ray diffraction (XRD), energy dispersive X-ray (EDX), and scanning electron microscopy (SEM). Finally, the activity of the prepared Sm2(MoO4)3 as photo-catalysts for the degradation of different organic dyes such as methyl orange (MO), methylene blue (MB), and rhodamine B (Rh B) was evaluated. The photocatalytic property of Sm2(MoO4)3/C3N4 and Sm2(MoO4)3/GO for the degradation of MO, was obtained. Based on the empirical data Sm2(MoO4)3/C3N4 had the strongest photodegradation effect as compared to the other compounds tested after around 40 min. BET analysis revealed that the specific surface area of the Sm2(MoO4)3 nanocomposite prepared using C3N4 is 15 times that of in the absence of C3N4. Also, the cytotoxicity of synthesized samples was evaluated using MTT assay against human cell lines MCF-7 (cancer), and its IC50 was about 125 mg/L. © 202

A modified sensitive carbon paste electrode for 5-fluorouracil based using a composite of praseodymium erbium tungstate

This paper describes the modification of a modified carbon paste electrode (CPE) using nanoparticles of praseodymium erbium tungstate (Pr:Er). The modified electrode was used for the sensitive voltammetric detection of an anticancer drug (5-fluorouracil (5-FU)) using. The modified-CPE was evaluated using cyclic voltammetry (CV), square wave voltammetry (SWV) and electrochemical impedance spectroscopy (EIS) and the resulting data showed the irreversible 5-fluorouracil oxidation peak around 1.0 V vs. Ag/AgCl. Some key parameters such as pH, the amount of the modifier, potential amplitude, step potential and frequency were studied and optimized. The square wave voltammetry (SWV) analytical calibration curve was linear in the range of 0.01�50 μM, with a detection limit of 0.98 nM analyses. The electron transfer coefficient (α) was also determined to be 0.76. The analyte concentration was also determined in pharmaceutical formulations and recovery percentages were found to be in the range of 96�102. The sensor had good reproducibility and repeatability with acceptable RSD values of 3.6, and 1.02 and a rather long-term stability of around one month. The as-synthesized nanoparticles were also characterized using FESEM, TEM, FTIR and XRD techniques. © 2020 Elsevier B.V

Specific fluorometric assay for direct determination of amikacin by molecularly imprinting polymer on high fluorescent g-C 3 N 4 quantum dots

Here, a specific and reliable fluorometric method for the rapid determination of amikacin was developed based on the molecularly imprinting polymer (MIP) capped g-C 3 N 4 quantum dots (QDs). g-C 3 N 4 QDs were obtained by facile and one-spot ethanol-thermal treatment of bulk g-C 3 N 4 powder and showed a high yield fluorescence emission under UV irradiation. The MIP layer was also created on the surface on QDs, via usual self-assembly process of 3-aminopropyl triethoxysilane (APTES) functional monomers and tetraethyl ortho-silicate (TEOS) cross linker in the presence of amikacin as template molecules. The synthesized MIP-QDs composite showed an improved tendency toward the amikacin molecules. In this state, amikacin molecules located adjacent to the g-C 3 N 4 QDs caused a remarkable quenching effect on the fluorescence emission intensity of QDs. This effect has a linear relationship with amikacin concentration and so, formed the basis of a selective assay to recognize amikacin. Under optimized experimental conditions, a linear calibration graph was obtained as the quenched emission and amikacin concentration, in the range of 3�400 ng mL �1 (4.4�585.1 nM) with a detection limit of 1.2 ng mL �1 (1.8 nM). The high selectivity of MIP sites as well as individual fluorescence properties of g-C 3 N 4 QDs offers a high specific and sensitive monitoring method for drug detection. The method was acceptably applied for the measurement of amikacin in biological samples. © 2019 Elsevier B.V

AAFACE: Attribute-aware Attentional Network for Face Recognition

In this paper, we present a new multi-branch neural network that simultaneously performs soft biometric (SB) prediction as an auxiliary modality and face recognition (FR) as the main task. Our proposed network named AAFace utilizes SB attributes to enhance the discriminative ability of FR representation. To achieve this goal, we propose an attribute-aware attentional integration (AAI) module to perform weighted integration of FR with SB feature maps. Our proposed AAI module is not only fully context-aware but also capable of learning complex relationships between input features by means of the sequential multi-scale channel and spatial sub-modules. Experimental results verify the superiority of our proposed network compared with the state-of-the-art (SoTA) SB prediction and FR methods.Comment: Accepted to $30^{th}$ IEEE International Conference on Image Processing (ICIP 2023) as an oral presentatio

Reliability and validity of the Persian version of the spinal cord injury lifestyle scale and the health behavior questionnaire in persons with spinal cord injury

Study design: Cross-sectional psychometric study. Objective: To evaluate the reliability and validity of a spinal cord injury lifestyle scale (SCILS) and Health Behavior Questionnaire (HBQ) in the Persian language for persons with spinal cord injury (SCI). Setting: Participants were selected among those referred to health centers and the Brain and Spinal Cord Injury Research Center. Method: In accordance with standard procedure for translation, two questionnaires, the SCILS and HBQ, were translated using a forward and backward translation approach by professional translators. Face validity of the questionnaires was assessed by ten persons with SCI and content validity was agreed upon by 12 professors from health care teaching universities. To test the final versions of both questionnaires, 97 persons with SCI were included using a consecutive sampling method. Other questionnaires were used to assess concurrent validity (secondary impairment checklist, as well as SCILS and HBQ) and convergent validity (impact of event scale revised, brief symptom inventory, beck depression inventory, and functional independence measure). Results: Internal consistency of SCILS and HBQ, assessed by Cronbach's alpha, was 0.75 for SCILS and 0.85 for HBQ. Test-retest reliability intraclass correlations were 0.86 and 0.92 for SCILS and HBQ, respectively. The number of current secondary impairments had a significant and negative correlation with SCILS (r =-0.22, P < 0.001), but it was not correlated with HBQ. SCILS had a significant and strong correlation with HBQ (r = 0.65, P < 0.001). Conclusion: SCILS and HBQ can be used for measuring the health behavior of persons with SCI in Iran. © 2018 International Spinal Cord Society

Electrochemical determination of the antipsychotic medication clozapine by a carbon paste electrode modified with a nanostructure prepared from titania nanoparticles and copper oxide

A nanostructure was prepared from titania nanoparticles and copper oxide (TiO2NP@CuO) and used to modify a carbon paste electrode (CPE). The modified CPE is shown to enable sensitive voltammetric determination of the drug clozapine (CLZ). The sensor was characterized by various techniques and some key parameters were optimized. Under the optimum conditions and at a working potential of 0.6 V (vs. Ag/AgCl), the modified CPE has two linear response ranges, one from 30 pmol L�1 to 4 nmol L�1 of CLZ, the other from 4 nmol L�1 to 10 μmol L�1. The detection limit is as low as 9 pM. The transfer coefficient (α) and catalytic rate constant (kcat) were calculated and the reliability of the sensor was estimated for CLZ sensing in real samples where it gave satisfactory results. Figure not available: see fulltext.. © 2019, Springer-Verlag GmbH Austria, part of Springer Nature