Multi-feature fusion for thermal face recognition

Bi, Yin; Lv, Mingsong; Wei, Yangjie; Guan, Nan; Wang, Yi

doi:10.1016/j.infrared.2016.05.011

Cited by 41 publications

(12 citation statements)

References 27 publications

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“…The fuzzy c‐means technique is considered as a method of clustering with robust framework, which gives better results for overlapped data. The method was coined by Dunn and was further developed by Bezdek . The fuzzy c‐means algorithm for spatial domain phase consists of following steps:…”

Section: Methodsmentioning

confidence: 99%

Computer‐aided diagnosis of retinal diseases using multidomain feature fusion

Keerthiveena

Esakkirajan

Selvakumar

et al. 2019

Int J Imaging Syst Tech

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In this article, an attempt is made to combine both transform and spatial domain features to increase the accuracy, sensitivity, and specificity for the classification of retinal diseases. Papilloedema, macular edema, glaucoma, diabetic retinopathy (DR), and central retinal vein occlusion (CRVO) are the leading cause of blindness in humans. To identify these diseases in a mass screening process, it consumes intense labor and time. In this proposed work, an automated computer aided algorithm is developed to classify the normal from the abnormal (Papilledema, macular edema, glaucoma, DR, and CRVO) images. For any classification task, an increase in data size, number of classes, and dimension of the feature space affect the performance of any classifier.A single classifier is generally unable to handle the wide variability and scalability of the data in any problem. In the proposed algorithm, the feature fusion technique is employed to reduce the input data to the classifier. The features are extracted from wavelet packet transform (WPT) and intensity hue saturation (IHS). A support vector machine (SVM)-based learning algorithm is used to train the classifier using the extracted features. The proposed method yields better results than the existing classifier fusion method with an overall accuracy, sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and area under ROC curve (AUC) of 96.3%, 95.8%, 97.8%, 96.4%,98.1%, and 0.971 respectively. K E Y W O R D Sfeature fusion, intensity hue saturation, support vector machine, wavelet packet transform, weighted principal component analysis

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Section: Methodsmentioning

confidence: 99%

Computer‐aided diagnosis of retinal diseases using multidomain feature fusion

Keerthiveena

Esakkirajan

Selvakumar

et al. 2019

Int J Imaging Syst Tech

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“…As far as face identification in LWIR is concerned, Bi et al focused on the multi-feature fusion technique. They used four methods of feature extraction: LBP, Gabor jet descriptor, Weber local descriptor, and a down sampling feature [ 27 ]. The decision function responsible algorithm was the sparse representation classifier.…”

Section: Related Workmentioning

confidence: 99%

Thermal Face Verification through Identification

Grudzień

Kowalski

Pałka

2021

Sensors

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This paper reports on a new approach to face verification in long-wavelength infrared radiation. Two face images were combined into one double image, which was then used as an input for a classification based on neural networks. For testing, we exploited two external and one homemade thermal face databases acquired in various variants. The method is reported to achieve a true acceptance rate of about 83%. We proved that the proposed method outperforms other studied baseline methods by about 20 percentage points. We also analyzed the issue of extending the performance of algorithms. We believe that the proposed double image method can also be applied to other spectral ranges and modalities different than the face.

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“…e projection components of the random variables in the original space are not correlated in the correlation subspace, and thus, the dimension of the generated eigenvector is higher than that of the original eigenvector [32]. 6 Complexity erefore, the dimension of the new feature should be reduced.…”

Section: Feature Fusion Canonical Correlation Analysis (Cca)mentioning

confidence: 99%

Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Plantar Pressure

Jiang

Lü

et al. 2020

Complexity

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Falls among the elderly comprise a major health problem. Daily activity monitoring and fall detection using wearable sensors provide an important healthcare system for elderly or frail individuals. We investigated the classification accuracy of daily activity and fall data based on surface electromyography (sEMG) and plantar pressure signals. sEMG and plantar pressure signals were collected, and their features were extracted. Suitable features were selected and combined for posture transition, gait, and fall using the Fisher class separability index. A feature-level fusion method, named as the global canonical correlation analysis of weighting genetic algorithm, was proposed to reduce dimensions. For the problem in which the number of daily activities is considerably more than the number of fall activities, Weighted Kernel Fisher Linear Discriminant Analysis (WKFDA) was proposed to classify gait and fall. Double Parameter Kernel Optimization based on Extreme Learning Machine (DPK-OMELM) was used to classify activities. Results showed that the classification accuracy of the posture transition is 100%, and the accuracy of gait and fall classified using WKFDA can reach 98%. For all types of posture transition, gait, and fall, sensitivity, specificity, and accuracy are over 96%.

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Multi-feature fusion for thermal face recognition

Cited by 41 publications

References 27 publications

Computer‐aided diagnosis of retinal diseases using multidomain feature fusion

Computer‐aided diagnosis of retinal diseases using multidomain feature fusion

Thermal Face Verification through Identification

Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Plantar Pressure

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