Recognizing Injured Faces via SCIFI Loss

Majumdar, Puspita; Chhabra, Saheb; Singh, Richa; Vatsa, Mayank

doi:10.1109/tbiom.2020.3047274

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…Tables III and IV show the performance of filtering technique in terms of MSE, PSNR, SSIM, PRD and SNR. In soft coring filter MSE, SNR and PRD values were low when compared with median filter technique whereas PSNR and SSIM values were high compared with median filter technique [36]. This graph can be used to determine how well filtering methods based on the MSE, PSNR, SSIM, PRD, and SNR perform (shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…ANFIS performed faster than the other categorization algorithms in a comparison. Each data set took 36,34,35,32,34,33,34,31,32,34,35, and 36 seconds to execute in seconds. The total effectiveness of several classification algorithms utilized for face recognition categorization is shown in Table XI and Fig.…”

Section: Resultsmentioning

confidence: 99%

“…15. According to a comparison of categorization techniques, ANFIS had the lowest average number of iterations of all methods, with 39,38,37,37,39,38,38,37,37,36,38,37,36,37,38 and 36 of data sets. 18 show the overall performance based on error rate of different classification technique employed for classification of data sets.…”

Section: Resultsmentioning

confidence: 99%

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Combination of Adaptive Neuro Fuzzy Inference System and Machine Learning Algorithm for Recognition of Human Facial Expressions

Dhanalaxmi,

Madhuravani,

Raju

et al. 2023

IJACSA

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A face recognition system's initial three processes are face detection, feature extraction, and facial expression recognition. The initial step of face detection involves colour model skin colour detection, lighting adjustment to achieve uniformity on the face, and morphological techniques to maintain the necessary face region. To extract facial characteristics such the eyes, nose, and mouth, the output of the first step is employed. Third-step methodology using automated face emotion recognition. This study's goal is to apply the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm to increase the precision of the current face recognition systems. For the purpose of removing noise and unwanted information from the data sets, independent data sets and a pre-processing technique are built in this study based on color, texture, and shape, to determine the features of the face. The output of the three-feature extraction process is given to the ANFIS model as input. By using our training picture data sets, it has already been trained. This model receives a test image as input, then evaluates the three aspects of the input image, and then recognizes the test image based on correlation. The determination of whether input has been authenticated or not is made using fuzzy logic. The proposed ANFIS method is compared to the existing methods such as Minimum Distance Classifier (MDC), Support Vector Machine (SVM), Case Based Reasoning (CBR) with the following quality measure like error rate, accuracy, precision, recall. Finally, the performance is analyzed by combining all feature extractions with existing classification methods such as MDC, KNN (K-Nearest Neighbour), SVM, ANFIS and CBR. Based on the performance of classification techniques, it is observed that the face detection failures are reduced, such that overall accuracy for CBR is 92% and it is 97% in ANFIS.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Combination of Adaptive Neuro Fuzzy Inference System and Machine Learning Algorithm for Recognition of Human Facial Expressions

Dhanalaxmi,

Madhuravani,

Raju

et al. 2023

IJACSA

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“…Face recognition algorithms have achieved tremendous success in handling different covariates such as low resolution, pose, expression, illumination [1,2,3,4]. It is now used in various applications such as surveillance, access control, forensics, and e-payments and researchers have been enhancing the state-of-the-art performance on challenging tasks [5,6,7,8]. However, the problem of recognizing faces under partial or heavy occlusion is still considered a challenging task [9].…”

Section: Introductionmentioning

confidence: 99%

Indian Masked Faces in the Wild Dataset

Mishra¹,

Majumdar

Singh

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Due to the COVID-19 pandemic, wearing face masks has become a mandate in public places worldwide. Face masks occlude a significant portion of the facial region. Additionally, people wear different types of masks, from simple ones to ones with graphics and prints. These pose new challenges to face recognition algorithms. Researchers have recently proposed a few masked face datasets for designing algorithms to overcome the challenges of masked face recognition. However, existing datasets lack the cultural diversity and collection in the unrestricted settings. Country like India with attire diversity, people are not limited to wearing traditional masks but also clothing like a thin cotton printed towel (locally called as "gamcha"), "stoles", and "handkerchiefs" to cover their faces. In this paper, we present a novel Indian Masked Faces in the Wild (IMFW) dataset which contains images with variations in pose, illumination, resolution, and the variety of masks worn by the subjects. We have also benchmarked the performance of existing face recognition models on the proposed IMFW dataset. Experimental results demonstrate the limitations of existing algorithms in presence of diverse conditions.

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