Multi-scale score level fusion of local descriptors for gender classification in the wild

Castrillón-Santana, Modesto; Lorenzo-Navarro, Javier; Ramón-Balmaseda, Enrique

doi:10.1007/s11042-016-3653-2

Cited by 15 publications

(7 citation statements)

References 44 publications

(71 reference statements)

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“…Later, a subset of descriptors is used to combine their decisions. According to our previous experience, the combination of descriptors has proven to be of interest in gender classification reducing both error and ambiguous cases [19], [20]. This evidence has also been argued by other authors in different applications.…”

Section: Classificationsupporting

confidence: 67%

Local descriptors fusion for mobile iris verification

Aginako

Martinez-Otzerta

Sierra

et al. 2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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Abstract-This paper summarizes the proposal submitted by the joint team conformed by researchers from UPV and ULPGC to the Mobile Iris CHallenge Evaluation II. The approach makes use of a state-of-the-art iris segmentation technique, to later extract features making use of local descriptors. Those suitable to the problem are selected after evaluating a collection of 15 local descriptors, covering a range of different grid configuration setups. A Machine Learning approach is used, learning a supervised classifier to deal with the descriptors data. A classifier is obtained for each descriptor, and the best ones are combined in a multi-classifier system. The final step fuses the classifier outputs obtained for 5 different local descriptors, to compute the dissimilarity measure for a pair of iris images.

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Section: Classificationsupporting

confidence: 67%

Local descriptors fusion for mobile iris verification

Aginako

Martinez-Otzerta

Sierra

et al. 2016

2016 23rd International Conference on Pattern Recognition (ICPR)

Self Cite

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“…In general, the data of a single modality lack uniqueness and non-universality and also comprise noise [49]. Therefore, multi-modal data obtained by the fusion of single modalities have better discrimination abilities and are used in various applications [50,51]. The fusion of the information can usually be performed at three levels: (a) feature-extraction level, (b) matching-score level, and (c) decision level.…”

Section: Final Classification Of Mitotic Cells Via Score-level Fusionmentioning

confidence: 99%

Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs

Mahmood

Arsalan

Owais

et al. 2020

JCM

139

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Breast cancer is the leading cause of mortality in women. Early diagnosis of breast cancer can reduce the mortality rate. In the diagnosis, the mitotic cell count is an important biomarker for predicting the aggressiveness, prognosis, and grade of breast cancer. In general, pathologists manually examine histopathology images under high-resolution microscopes for the detection of mitotic cells. However, because of the minute differences between the mitotic and normal cells, this process is tiresome, time-consuming, and subjective. To overcome these challenges, artificial-intelligence-based (AI-based) techniques have been developed which automatically detect mitotic cells in the histopathology images. Such AI techniques accelerate the diagnosis and can be used as a second-opinion system for a medical doctor. Previously, conventional image-processing techniques were used for the detection of mitotic cells, which have low accuracy and high computational cost. Therefore, a number of deep-learning techniques that demonstrate outstanding performance and low computational cost were recently developed; however, they still require improvement in terms of accuracy and reliability. Therefore, we present a multistage mitotic-cell-detection method based on Faster region convolutional neural network (Faster R-CNN) and deep CNNs. Two open datasets (international conference on pattern recognition (ICPR) 2012 and ICPR 2014 (MITOS-ATYPIA-14)) of breast cancer histopathology were used in our experiments. The experimental results showed that our method achieves the state-of-the-art results of 0.876 precision, 0.841 recall, and 0.858 F1-measure for the ICPR 2012 dataset, and 0.848 precision, 0.583 recall, and 0.691 F1-measure for the ICPR 2014 dataset, which were higher than those obtained using previous methods. Moreover, we tested the generalization capability of our technique by testing on the tumor proliferation assessment challenge 2016 (TUPAC16) dataset and found that our technique also performs well in a cross-dataset experiment which proved the generalization capability of our proposed technique.

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“…A fused vector of match scores is treated as a feature vector which is then classified into one of two classes: 'genuine user' or 'impostor'. Various classifiers used for this purpose are support vector machine (SVM) [24,25] and its variants, Fisher linear discriminant analysis, the Bayesian classifier (beta distribution), multi-layer perceptron and decision tree [25], hidden Markov model (HMM) [26] etc. Artabaz et al [27] combined different primitive score-level fusion rules using a genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems

2018

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Human recognition in a multi-biometric system is performed by combining biometric clues from different sources (multiple sensors, units, algorithms, samples and modalities) at different levels (sensor, feature, score, rank and decision level). Low computational complexity and adequate data for fusion make the score-level fusion a preferable option over other levels of fusion. However, incompatibility issue prevails at this level as scores obtained from different uni-biometric systems are disparate in nature. This disparity can be resolved by using score normalisation before fusion. This study first analysed the effect of generalised extreme value distribution-based score normalisation technique on different fusion techniques and then proposes an efficient score fusion technique based on Dezert-Smarandache theory (DSmT). A unique blend of belief assignment and decision-making methods in the DSmT framework is proposed for score-level fusion. For evaluation of the proposed method, experiments are performed on multi-algorithm, multi-unit and multi-modal biometrics systems created from three publicly available datasets: (i) NIST BSSR1 multi-biometric score database, (ii) face recognition grand challenge (FRGC) V2.0 and (iii) LG4000 iris dataset. Comparative studies of performance analysis show the efficiency of the authors' proposed method over other recently published state-of-the-art fusion methods.

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Multi-scale score level fusion of local descriptors for gender classification in the wild

Cited by 15 publications

References 44 publications

Local descriptors fusion for mobile iris verification

Local descriptors fusion for mobile iris verification

Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs

Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems

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