Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00126
Nadia Burkart, Marco F. Huber, Phillip Faller
Remarkable progress in the field of machine learning strongly drives the research in many application domains. For some domains, it is mandatory that the output of machine learning algorithms needs to be interpretable. In this paper, we propose a rule-based regularization technique to enforce interpretability for neural networks (NN). For this purpose, we train a rule-based surrogate model simultaneously with the NN. From the surrogate, a metric quantifying its degree of explainability is derived and fed back to the training of the NN as a regularization term. We evaluate our model on four datasets and compare it to unregularized models as well as a decision tree (DT) based baseline. The rule-based regularization approach achieves interpretability and competitive accuracy.
{"title":"Forcing Interpretability for Deep Neural Networks through Rule-Based Regularization","authors":"Nadia Burkart, Marco F. Huber, Phillip Faller","doi":"10.1109/ICMLA.2019.00126","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00126","url":null,"abstract":"Remarkable progress in the field of machine learning strongly drives the research in many application domains. For some domains, it is mandatory that the output of machine learning algorithms needs to be interpretable. In this paper, we propose a rule-based regularization technique to enforce interpretability for neural networks (NN). For this purpose, we train a rule-based surrogate model simultaneously with the NN. From the surrogate, a metric quantifying its degree of explainability is derived and fed back to the training of the NN as a regularization term. We evaluate our model on four datasets and compare it to unregularized models as well as a decision tree (DT) based baseline. The rule-based regularization approach achieves interpretability and competitive accuracy.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123988394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00076
Sanket Shukla, Gaurav Kolhe, Sai Manoj Pudukotai Dinakarrao, S. Rafatirad
Malware detection and classification has enticed a lot of researchers in the past decades. Several mechanisms based on machine learning (ML), computer vision and deep learning have been deployed to this task and have achieved considerable results. However, advanced malware (stealthy malware) generated using various obfuscation techniques like code relocation, code transposition, polymorphism and mutation thwart the detection. In this paper, we propose a two-pronged technique which can efficiently detect both traditional and stealthy malware. Firstly, we extract the microarchitectural traces procured while executing the application, which are fed to the traditional ML classifiers to identify malware spawned as separate thread. In parallel, for an efficient stealthy malware detection, we instigate an automated localized feature extraction technique that will be used as an input to recurrent neural networks (RNNs) for classification. We have tested the proposed mechanism rigorously on stealthy malware created using code relocation obfuscation technique. With the proposed two-pronged approach, an accuracy of 94%, precision of 93%, recall score of 96% and F-1 score of 94% is achieved. Furthermore, the proposed technique attains up to 11% higher on average detection accuracy and precision, along with 24% higher on average recall and F-1 score as compared to the CNN-based sequence classification and hidden Markov model (HMM) based approaches in detecting stealthy malware.
{"title":"RNN-Based Classifier to Detect Stealthy Malware using Localized Features and Complex Symbolic Sequence","authors":"Sanket Shukla, Gaurav Kolhe, Sai Manoj Pudukotai Dinakarrao, S. Rafatirad","doi":"10.1109/ICMLA.2019.00076","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00076","url":null,"abstract":"Malware detection and classification has enticed a lot of researchers in the past decades. Several mechanisms based on machine learning (ML), computer vision and deep learning have been deployed to this task and have achieved considerable results. However, advanced malware (stealthy malware) generated using various obfuscation techniques like code relocation, code transposition, polymorphism and mutation thwart the detection. In this paper, we propose a two-pronged technique which can efficiently detect both traditional and stealthy malware. Firstly, we extract the microarchitectural traces procured while executing the application, which are fed to the traditional ML classifiers to identify malware spawned as separate thread. In parallel, for an efficient stealthy malware detection, we instigate an automated localized feature extraction technique that will be used as an input to recurrent neural networks (RNNs) for classification. We have tested the proposed mechanism rigorously on stealthy malware created using code relocation obfuscation technique. With the proposed two-pronged approach, an accuracy of 94%, precision of 93%, recall score of 96% and F-1 score of 94% is achieved. Furthermore, the proposed technique attains up to 11% higher on average detection accuracy and precision, along with 24% higher on average recall and F-1 score as compared to the CNN-based sequence classification and hidden Markov model (HMM) based approaches in detecting stealthy malware.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"393 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121787602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00239
Rosemarie J. Day, H. Salehi, Mahsa Javadi
Everyday migraines are affecting more than one billion people worldwide. This headache disorder is classified as the sixth most disabling disease in the world. Migraines are just one chronic illness affected by environmental triggers due to changes that occur inside the home. Migraines share this characteristic with sinus headaches and thus are often misdiagnosed. In this research work, an iOS-based environmental analyzer was designed, implemented and evaluated for migraine sufferers with the use of sensors. After the data collection and cleaning, five machine learning model were used to estimate prediction accuracy of migraines in terms of the environment. The data was evaluated against the models using K-Fold cross validation. The algorithm accuracy comparison showed that Linear Discriminant Analysis (LDA) produced highest accuracy for the testing data at a mean of 0.938. Preliminary results demonstrate the feasibility of using machine learning algorithms to perform the automated recognition of migraine trigger areas in the environment.
{"title":"IoT Environmental Analyzer using Sensors and Machine Learning for Migraine Occurrence Prevention","authors":"Rosemarie J. Day, H. Salehi, Mahsa Javadi","doi":"10.1109/ICMLA.2019.00239","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00239","url":null,"abstract":"Everyday migraines are affecting more than one billion people worldwide. This headache disorder is classified as the sixth most disabling disease in the world. Migraines are just one chronic illness affected by environmental triggers due to changes that occur inside the home. Migraines share this characteristic with sinus headaches and thus are often misdiagnosed. In this research work, an iOS-based environmental analyzer was designed, implemented and evaluated for migraine sufferers with the use of sensors. After the data collection and cleaning, five machine learning model were used to estimate prediction accuracy of migraines in terms of the environment. The data was evaluated against the models using K-Fold cross validation. The algorithm accuracy comparison showed that Linear Discriminant Analysis (LDA) produced highest accuracy for the testing data at a mean of 0.938. Preliminary results demonstrate the feasibility of using machine learning algorithms to perform the automated recognition of migraine trigger areas in the environment.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121335751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00111
Daniel Gutiérrez, S. Toral
Mobility plays an important role in the performance of wireless multi-hop networks. Since communications are established in a multi-hop fashion, the mobility of nodes can cause a significant degradation of the performance. Therefore, the analysis of nodes' mobility is relevant to improve the performance of the applications implemented over wireless multi-hop networks. This work evaluates two neuronal network models, such as fully connected or multi-layer perceptron and 1D convolutional models, for the classification of up to four widely used mobility models for wireless multi-hop networks. Several architectures are evaluated and parametrized for both models. The results indicate a considerable better performance of an architecture with 1D convolutional layers. The test results show that the best convolutional 1D model is able to reach an accuracy level of 0.91, outperforming the best multi-layer perceptron model in 13,9 %.
{"title":"Deep Neuronal Based Classifiers for Wireless Multi-hop Network Mobility Models","authors":"Daniel Gutiérrez, S. Toral","doi":"10.1109/ICMLA.2019.00111","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00111","url":null,"abstract":"Mobility plays an important role in the performance of wireless multi-hop networks. Since communications are established in a multi-hop fashion, the mobility of nodes can cause a significant degradation of the performance. Therefore, the analysis of nodes' mobility is relevant to improve the performance of the applications implemented over wireless multi-hop networks. This work evaluates two neuronal network models, such as fully connected or multi-layer perceptron and 1D convolutional models, for the classification of up to four widely used mobility models for wireless multi-hop networks. Several architectures are evaluated and parametrized for both models. The results indicate a considerable better performance of an architecture with 1D convolutional layers. The test results show that the best convolutional 1D model is able to reach an accuracy level of 0.91, outperforming the best multi-layer perceptron model in 13,9 %.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122261454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00246
Achille Salaün, Y. Petetin, F. Desbouvries
Recurrent Neural Networks (RNN) and Hidden Markov Models (HMM) are popular models for processing sequential data and have found many applications such as speech recognition, time series prediction or machine translation. Although both models have been extended in several ways (eg. Long Short Term Memory and Gated Recurrent Unit architectures, Variational RNN, partially observed Markov models...), their theoretical understanding remains partially open. In this context, our approach consists in classifying both models from an information geometry point of view. More precisely, both models can be used for modeling the distribution of a sequence of random observations from a set of latent variables; however, in RNN, the latent variable is deterministically deduced from the current observation and the previous latent variable, while, in HMM, the set of (random) latent variables is a Markov chain. In this paper, we first embed these two generative models into a generative unified model (GUM). We next consider the subclass of GUM models which yield a stationary Gaussian observations probability distribution function (pdf). Such pdf are characterized by their covariance sequence; we show that the GUM model can produce any stationary Gaussian distribution with geometrical covariance structure. We finally discuss about the modeling power of the HMM and RNN submodels, via their associated observations pdf: some observations pdf can be modeled by a RNN, but not by an HMM, and vice versa; some can be produced by both structures, up to a re-parameterization.
{"title":"Comparing the Modeling Powers of RNN and HMM","authors":"Achille Salaün, Y. Petetin, F. Desbouvries","doi":"10.1109/ICMLA.2019.00246","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00246","url":null,"abstract":"Recurrent Neural Networks (RNN) and Hidden Markov Models (HMM) are popular models for processing sequential data and have found many applications such as speech recognition, time series prediction or machine translation. Although both models have been extended in several ways (eg. Long Short Term Memory and Gated Recurrent Unit architectures, Variational RNN, partially observed Markov models...), their theoretical understanding remains partially open. In this context, our approach consists in classifying both models from an information geometry point of view. More precisely, both models can be used for modeling the distribution of a sequence of random observations from a set of latent variables; however, in RNN, the latent variable is deterministically deduced from the current observation and the previous latent variable, while, in HMM, the set of (random) latent variables is a Markov chain. In this paper, we first embed these two generative models into a generative unified model (GUM). We next consider the subclass of GUM models which yield a stationary Gaussian observations probability distribution function (pdf). Such pdf are characterized by their covariance sequence; we show that the GUM model can produce any stationary Gaussian distribution with geometrical covariance structure. We finally discuss about the modeling power of the HMM and RNN submodels, via their associated observations pdf: some observations pdf can be modeled by a RNN, but not by an HMM, and vice versa; some can be produced by both structures, up to a re-parameterization.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126666298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00089
A. Muthukumar, Ramakrishnan Durairajan
To understand the delay characteristics of the Internet, a myriad of measurement tools and techniques are proposed by the researchers in academia and industry. Datasets from such measurement tools are curated to facilitate analyses at a later time. Despite the benefits of these tools and datasets, the systematic interpretation of measurements in the face of measurement noise. Unfortunately, state-of-the-art denoising techniques are labor-intensive and ineffective. To tackle this problem, we develop NoMoNoise, an open-source framework for denoising latency measurements by leveraging the recent advancements in weak-supervised learning. NoMoNoise can generate measurement noise labels that could be integrated into the inference and control logic to remove and/or repair noisy measurements in an automated and rapid fashion. We evaluate the efficacy of NoMoNoise in a lab-based setting and a real-world setting by applying it on CAIDA's Ark dataset and show that NoMoNoise can remove noisy measurements effectively with high accuracy.
{"title":"Denoising Internet Delay Measurements using Weak Supervision","authors":"A. Muthukumar, Ramakrishnan Durairajan","doi":"10.1109/ICMLA.2019.00089","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00089","url":null,"abstract":"To understand the delay characteristics of the Internet, a myriad of measurement tools and techniques are proposed by the researchers in academia and industry. Datasets from such measurement tools are curated to facilitate analyses at a later time. Despite the benefits of these tools and datasets, the systematic interpretation of measurements in the face of measurement noise. Unfortunately, state-of-the-art denoising techniques are labor-intensive and ineffective. To tackle this problem, we develop NoMoNoise, an open-source framework for denoising latency measurements by leveraging the recent advancements in weak-supervised learning. NoMoNoise can generate measurement noise labels that could be integrated into the inference and control logic to remove and/or repair noisy measurements in an automated and rapid fashion. We evaluate the efficacy of NoMoNoise in a lab-based setting and a real-world setting by applying it on CAIDA's Ark dataset and show that NoMoNoise can remove noisy measurements effectively with high accuracy.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125649665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00107
Ze Jin, D. Matteson, Tianrong Zhang
We apply both distance-based and kernel-based mutual dependence measures to independent component analysis (ICA), and generalize dCovICA to MDMICA, minimizing empirical dependence measures as an objective function in both deflation and parallel manners. Solving this minimization problem, we introduce Latin hypercube sampling (LHS), and a global optimization method, Bayesian optimization (BO) to improve the initialization of the Newton-type local optimization method. The performance of MDMICA is evaluated in various simulation studies and an image data example. When the ICA model is correct, MDMICA achieves competitive results compared to existing approaches. When the ICA model is misspecified, the estimated independent components are less mutually dependent than the observed components using MDMICA, while the estimated independent components are prone to be even more mutually dependent than the observed components using other approaches.
{"title":"Independent Component Analysis Based on Mutual Dependence Measures","authors":"Ze Jin, D. Matteson, Tianrong Zhang","doi":"10.1109/ICMLA.2019.00107","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00107","url":null,"abstract":"We apply both distance-based and kernel-based mutual dependence measures to independent component analysis (ICA), and generalize dCovICA to MDMICA, minimizing empirical dependence measures as an objective function in both deflation and parallel manners. Solving this minimization problem, we introduce Latin hypercube sampling (LHS), and a global optimization method, Bayesian optimization (BO) to improve the initialization of the Newton-type local optimization method. The performance of MDMICA is evaluated in various simulation studies and an image data example. When the ICA model is correct, MDMICA achieves competitive results compared to existing approaches. When the ICA model is misspecified, the estimated independent components are less mutually dependent than the observed components using MDMICA, while the estimated independent components are prone to be even more mutually dependent than the observed components using other approaches.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125830222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00291
Iuliia Gavriushina, Oliver R. Sampson, M. Berthold, W. Pohlmeier, C. Borgelt
Index investing has an advantage over active investment strategies, because less frequent trading results in lower expenses, yielding higher long-term returns. Index tracking is a popular investment strategy that attempts to find a portfolio replicating the performance of a collection of investment vehicles. This paper considers index tracking from the perspective of solution space exploration. Three search space heuristics in combination with three portfolio tracking error methods are compared in order to select a tracking portfolio with returns that mimic a benchmark index. Experimental results conducted on real-world datasets show that Widening, a metaheuristic using diverse parallel search paths, finds superior solutions than those found by the reference heuristics. Presented here are the first results using Widening on time-series data.
{"title":"Widened Learning of Index Tracking Portfolios","authors":"Iuliia Gavriushina, Oliver R. Sampson, M. Berthold, W. Pohlmeier, C. Borgelt","doi":"10.1109/ICMLA.2019.00291","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00291","url":null,"abstract":"Index investing has an advantage over active investment strategies, because less frequent trading results in lower expenses, yielding higher long-term returns. Index tracking is a popular investment strategy that attempts to find a portfolio replicating the performance of a collection of investment vehicles. This paper considers index tracking from the perspective of solution space exploration. Three search space heuristics in combination with three portfolio tracking error methods are compared in order to select a tracking portfolio with returns that mimic a benchmark index. Experimental results conducted on real-world datasets show that Widening, a metaheuristic using diverse parallel search paths, finds superior solutions than those found by the reference heuristics. Presented here are the first results using Widening on time-series data.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125235128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00127
Yuriy Mishchenko, Yusuf Goren, Ming Sun, Chris Beauchene, Spyros Matsoukas, Oleg Rybakov, S. Vitaladevuni
In this paper, we investigate novel quantization approaches to reduce memory and computational footprint of deep neural network (DNN) based keyword spotters (KWS). We propose a new method for KWS offline and online quantization, which we call dynamic quantization, where we quantize DNN weight matrices column-wise, using each column's exact individual min-max range, and the DNN layers' inputs and outputs are quantized for every input audio frame individually, using the exact min-max range of each input and output vector. We further apply a new quantization-aware training approach that allows us to incorporate quantization errors into KWS model during training. Together, these approaches allow us to significantly improve the performance of KWS in 4-bit and 8-bit quantized precision, achieving the end-to-end accuracy close to that of full precision models while reducing the models' on-device memory footprint by up to 80%.
{"title":"Low-Bit Quantization and Quantization-Aware Training for Small-Footprint Keyword Spotting","authors":"Yuriy Mishchenko, Yusuf Goren, Ming Sun, Chris Beauchene, Spyros Matsoukas, Oleg Rybakov, S. Vitaladevuni","doi":"10.1109/ICMLA.2019.00127","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00127","url":null,"abstract":"In this paper, we investigate novel quantization approaches to reduce memory and computational footprint of deep neural network (DNN) based keyword spotters (KWS). We propose a new method for KWS offline and online quantization, which we call dynamic quantization, where we quantize DNN weight matrices column-wise, using each column's exact individual min-max range, and the DNN layers' inputs and outputs are quantized for every input audio frame individually, using the exact min-max range of each input and output vector. We further apply a new quantization-aware training approach that allows us to incorporate quantization errors into KWS model during training. Together, these approaches allow us to significantly improve the performance of KWS in 4-bit and 8-bit quantized precision, achieving the end-to-end accuracy close to that of full precision models while reducing the models' on-device memory footprint by up to 80%.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128708618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00312
O. Iloanusi, C. Mbah
High gender classification accuracies have been recorded with high-resolution faces under controlled conditions. However, real-life scenarios are faced with challenges not limited to high pose variations in subjects, poor visibility, occlusion, and distance from camera. These have led to the current trend in estimating gender from full body images, notwithstanding the challenges posed by partial body images in a typical life scenario. We demonstrate that there are certain sections in a body image, the face, upper or lower body that are useful for recognition at near or far distances. Given the challenges of body captured at far distance or partially showing body in a photo, we therefore propose a combination of three classifiers for gender estimation from face; upper and full body from single-shot image. Our results in far compared to near distance images suggest that gender is best estimated from a hybrid of face; upper and full body images under challenging conditions.
{"title":"Gender Estimation from a Hybrid of Face, Upper and Full Body Images at Varying Body Poses","authors":"O. Iloanusi, C. Mbah","doi":"10.1109/ICMLA.2019.00312","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00312","url":null,"abstract":"High gender classification accuracies have been recorded with high-resolution faces under controlled conditions. However, real-life scenarios are faced with challenges not limited to high pose variations in subjects, poor visibility, occlusion, and distance from camera. These have led to the current trend in estimating gender from full body images, notwithstanding the challenges posed by partial body images in a typical life scenario. We demonstrate that there are certain sections in a body image, the face, upper or lower body that are useful for recognition at near or far distances. Given the challenges of body captured at far distance or partially showing body in a photo, we therefore propose a combination of three classifiers for gender estimation from face; upper and full body from single-shot image. Our results in far compared to near distance images suggest that gender is best estimated from a hybrid of face; upper and full body images under challenging conditions.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132396798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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