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Face Identification via Strategic Combination of Local Features 1st

Face Identification via Strategic Combination of Local Featu...

引用

1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Rakshit, Rinku Datta Kisku, Dakshina Ranjan Asansol Engineering College Vivekananda Sarani Kanyapur AsansolWest Bengal713305 India National Institute of Technology Mahatma Gandhi Road A-Zone DurgapurWest Bengal713209 India

ISBN: (纸本)9789811390418

Face identification systems that use a single local descriptor often suffer from lack of well-structured, complementary and relevant facial descriptors. To achieve notable performance, a face identification system is presented, which makes use of multiple representations of a face by means of different local descriptors derived from Local Binary pattern (LBP) and Local Graph Structure (LGS). Then, max, average and L2 pooling operations are applied on each representation of face image to scale down the features. Further, different representations are combined together and formed a strategically concatenated feature vector. Three classifiers including two correlation based and k-nearest neighbor (kNN) are used to produce matching proximities which are used to characterize a probe user with rank identity. The produced ranks are then fused together using rank level fusion techniques. The experimental results determined on the Extended Yale face B database and the Plastic Surgery database are encouraging and convincing. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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Gas Discrimination & Quantification using Sensor Array with 3D Convolution Regression Dual Network

Gas Discrimination & Quantification using Sensor Array with ...

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IEEE international Workshop on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications

作者： Vishakha Pareek Santanu Chaudhury Sanjay Singh CSIR - Central Electronics Engineering Research Institute Pilani India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad India Indian Institute of Technology Delhi India

ISBN: (纸本)9781665426060

Smart sensor system design requires intelligent data processing, which analyzes raw time-series sensor data to efficiently and precisely discriminate and quantify target gases. The work presented here utilizes the response of twin gas sensor arrays for gases such as ethanol, ethylene, methane, and carbon monoxide to discriminate and quantify the target gases. We propose a 3D convolution neural-based regression dual network (3D-CNRDN) for both gas quantification and discrimination. The spatiotemporal correlation of sensor array responses inspired us to design the deep neural network for the gas concentration estimation model. The sensor array set is spatially correlated, and all the twin array responses are temporally related. 3D-CNRDN uses raw time-series gas sensor array data. The data is fed to the network as the 3D pattern, which contains 2D spatial information varying with third dimension time to recognize patterns that eventually predict the concentration. The model evaluation shows that the proposed methods are an effective technique for gas quantification and identification with $\text{RMSE}=0.3179$ and classification accuracy 94.37%. Furthermore, the proposed method outperforms and provides higher discrimination accuracy and lower RMSE than other machine learning and deep learning methods.

关键词： Deep learning Gases Three-dimensional displays Convolution Neural networks Spatiotemporal phenomena pattern recognition

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Real-Time Texture Extraction Based on the Improved Median Robust Extended Local Binary pattern 2020

Real-Time Texture Extraction Based on the Improved Median Ro...

引用

Proceedings of the 2020 9th international conference on computing and pattern recognition

作者： Mengnan Wang Yanjun Zhang School of Information and Electronics Beijing Institute of Technology Beijing China CN

ISBN: (纸本)9781450387835

Local binary patterns (LBP) are considered as the most computation efficient and high-performance texture features. Among all variants of the LBPs, Median Robust Extended Local Binary pattern (MRELBP) [1] is considered as one of the highest-performance one. Based on the MRELBP, this paper proposes an improved real-time texture extraction architecture implemented on a field-programmable gate array (FPGA) device. The fixed-point fraction is used to replace the double float-point fraction, only results in less than 1% difference to the accuracy. The proposed system is implemented on Xilinx Zynq-7045, and the extraction time is linear with the size of the input image. The extraction time of the 128x128 image in the dataset Outex-TC is merely 85.64μs, which is more than 4800 times faster than Liu et al. implemented on MATLAB [2].

关键词： real-time MRELBP feature extraction Field-Programmable Gate Array (FPGA)

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pattern recognition Through Digital Image Processing for Unmanned Aerial Vehicles 26

Pattern Recognition Through Digital Image Processing for Unm...

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IEEE 26th international conference on Electronics, Electrical Engineering and computing (INTERCON)

作者： Ponce, Gabino Rey Vidangos Bhimani, Kishankumar Prakosa, Jalu A. Ana Beatriz Alvarez, M. St Petersburg Electrotech Univ LETI Fac Elect Engn & Automat St Petersburg Russia St Petersburg Electrotech Univ LETI Dept Robot & Automat Ind Syst St Petersburg Russia Indonesian Inst Sci LIPI Res Ctr Phys Tangerang Selatan Indonesia Univ Fed Acre UFAC Ctr Ciencias Exatas & Tecnol CCET Aldeia Do Rio Branc Brazil

ISBN: (纸本)9781728136462

This paper describes the implementation of two digital image processing methods for pattern recognition, by color boundary method and the Haar Cascade Classifier to detect objects in a video stream, both methods implemented on Python 3 and OpenCV. patterns detection of images obtained from drones has advantages over traditional video recording drones. The drone has a streaming video system, based on the Raspberry Pi 3 minicomputer, which is sent by wireless communication to the base station where a pattern recognition algorithm performs operations on the video source coming from the drone. This proposed system has excellent performance based on an integrated streaming video system with 5.8GHz Wi-Fi connection acceptable to the base station. Both methods proved to be valid for certain types of patterns and objects under different light conditions.

关键词： Digital image processing pattern recognition Boundary color detection Haar Cascade method for customized object detection Video Streaming Wireless communication Aerial robots Drones

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7th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2018

7th International Conference on Frontiers of Intelligent Com...

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7th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2018

ISBN: (纸本)9789813291850

The proceedings contain 72 papers. The special focus in this conference is on Frontiers of Intelligent computing: Theory and Applications. The topics include: An Efficient Privacy-Preserving Search Technique for Encrypted Cloud Data;optimal Path Selection in Wireless Sensor Networks Using Enhanced Spatial Reusability;Application of Classical Codes over GF(4) on Quantum Error Correction Codes;Improved GLIM in Multiple-Input Multiple-Output OFDM VLC;design and Implementation of an IoT-Based Water Purifier System Enabling Predictive Maintenance;grey Wolf Optimized Task Scheduling Algorithm in Cloud computing;adaptive Feature Selection and Classification Using Optimization Technique;how to Fairly Allocate Indivisible Resources Among Agents Having Lexicographic Subadditive Utilities;a Novel Approach of Ontology-Based Activity Segmentation and recognition Using pattern Discovery in Multi-resident Homes;colorectal Cancer Diagnosis with Complex Fuzzy Inference System;a Machine Learning Approach for Hot Topic Detection in News;a Proposal of Expert System Using Deep Learning Neural Networks and Fuzzy Rules for Diagnosing Heart Disease;Development of the Rules for Model Transformation with OCL Integration in UWE;nonintrusive Load Monitoring Algorithms: A Comparative Study;craftQuest—Mobile App for Collecting Craft Village Data;tracking Big5 Traits Based on Mobile User Log Data;prediction of Factors Associated with the Dropout Rates of Primary to High School Students in India Using Data Mining Tools;domain-Specific Versus General-Purpose Word Representations in Sentiment Analysis for Deep Learning Models;improvement of Machine Learning Method by Combining Flow Text and Layout Text in Extracting Information from Scanned Healthcare Documents;assessing the Learning Difficulty of Text-Based Learning Materials;watermark by Learning Non-saliency.

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Accurate Trajectory Prediction in a Smart Building using Recurrent Neural Networks 20

Accurate Trajectory Prediction in a Smart Building using Rec...

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ACM international Joint conference on Pervasive and Ubiquitous computing (UbiComp) / ACM international Symposium on Wearable Computers (ISWC)

作者： Das, Anooshmita Kolvig-Raun, Emil Stubbe Kjaergaard, Mikkel Baun Univ Southern Denmark Odense Denmark

ISBN: (纸本)9781450380768

Occupant behavioral patterns, once extracted, could reveal cues about activities and space usage that could effectively get used for building systems to achieve energy savings. The ability to accurately predict the trajectories of occupants inside a room branched into different zones has many notable and compelling applications. For example - efficient space utilization and floor plans, intelligent building operations, crowd management, comfortable indoor environment, security, and evacuation or managing personnel. This paper proposes future occupant trajectory prediction using state-of-the-art time series prediction methods, i.e., Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. These models are being implemented and compared to forecast occupant trajectories at a given time and location in a non-intrusive and reliable manner. The considered test-space for the collection of the dataset is a multi-utility area in an instrumented public building. The deployed 3D Stereo Vision Cameras capture the spatial location coordinates (x- and y- coordinates) from a bird's view angle without eliciting any other information that could reveal confidential data or uniquely identify a person. Our results showed that the GRU model forecasts were considerably more accurate than the LSTM model for the trajectory prediction. GRU prediction model achieved a Mean Squared Error (MSE) of 30.72 cm between actual and predicted location coordinates, and LSTM achieved an MSE of 47.13 cm, respectively, for multiple occupant trajectories within the monitored area. Another evaluation metric Mean Absolute Error (MAE) is used, and the GRU prediction model achieved an MAE of 3.14 cm, and the LSTM model achieved an MAE of 4.07 cm. The GRU model guarantees a high-fidelity occupant trajectory prediction for any given case with higher accuracy when compared to the baseline LSTM model.

关键词： Occupant Behavior pattern recognition Knowledge Discovery Trajectory Prediction Prediction Models LSTM GRU Deep Learning

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Person Localization in an Indoor Environment with Artificial Intelligence 19

Person Localization in an Indoor Environment with Artificial...

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5th international conference on computing and Artificial Intelligence (ICCAI)

作者： Acevedo, Elena Orozco, Ricardo Acevedo, Antonio Felipe, Federico Inst Politecn Nacl Av IPN S-N Lindavista 07738 Mexico

ISBN: (纸本)9781450361064

Associative models are Artificial Intelligence tools and have been used in many applications such as pattern recognition, classification, encryption, among others. In this paper we applied these models to trace a person in an indoor environment by the means of the power of a Wi-Fi signal. We deal with this problem as a classification task. We used a preprocessing for the data to improve the results. Our performance was 95.75%.

关键词： Artificial Intelligence Associative Models pattern recognition Classification User Localization Indoor Environment

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The Elastic k-Nearest Neighbours Classifier for Touch Screen Gestures 18th

The Elastic k-Nearest Neighbours Classifier for Touch Screen...

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18th international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Rzecki, Krzysztof Siwik, Leszek Baran, Mateusz Cracow Univ Technol Fac Phys Math & Comp Sci Ul Warszawska 24 PL-31155 Krakow Poland AGH Univ Sci & Technol Dept Comp Sci Krakow Poland

ISBN: (纸本)9783030209124;9783030209117

Touch screen gestures are a well-known method of person authentication in mobile devices. In most applications it is, however, reduced to checking if the user entered the correct pattern. Using additional information based on the speed and shape of finger movements can provide higher security without significantly impacting the convenience of this authorization method. In this work a new distance function for the k-nearest neighbour (kNN) classifier is considered in the problem of person recognition based on touch screen gestures. The function is based on the well-known L-p distance and the elastic distance considered in elastic shape analysis. Performance of the classifier is measured using 5-fold stratified cross-validation on a set of 12 people. Only four gesture performances per gesture for each person are used to train a model. The effects of sampling rate on the classifier performance is also measured. The kNN classifier with the proposed distance function has higher accuracy than both the L-p distance and the elastic distance.

关键词： Touch screen gestures Biometrics Classification Elastic shape analysis pattern recognition

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Coniferous Trees Needles-Based Taxonomy Classification

Coniferous Trees Needles-Based Taxonomy Classification

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international conference on Image and Vision computing New Zealand (IVCNZ)

作者： Haindl, Michal Zid, Pavel Czech Acad Sci Inst Informat Theory & Automat Pattern Recognit Dept Prague Czech Republic

ISBN: (纸本)9781728141879

This paper introduces multispectral rotationally invariant textural features of the Markovian type applied for the effective coniferous tree needles categorization. Presented texture features are inferred from the descriptive multispectral spiral wide-sense Markov model. Unlike the alternative texture recognition methods based on various gray-scale discriminative textural descriptions, we take advantage of the needles texture representation, which is fully descriptive multispectral and rotationally invariant. The presented method achieves high accuracy for needles recognition. Thus it can be used for reliable coniferous tree taxon classification. Our classifier is tested on the open source needles database Aff, which contains 716 high-resolution images from 11 diverse coniferous tree species.

关键词： Coniferous needles categorization Tree taxonomy recognition Spiral Markov random field model

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An Effective Pipeline for Pedestrian Detection in Mid-High Density Crowd 25

An Effective Pipeline for Pedestrian Detection in Mid-High D...

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25th IEEE international conference on Automation and computing (IEEE ICAC)

作者： Hao, Yu Gledhill, Duke Liu, Ying Fan, Jiulun Xu, Zhijie Xian Univ Posts & Telecommun Sch Comp Sci Xian Peoples R China Univ Huddersfield Sch Comp & Engn Huddersfield W Yorkshire England

ISBN: (纸本)9781861376657

A novel framework is introduced to handle the pedestrian detection in mid-high crowd density. This framework exploits the samples obtained with the unsupervised approach, extracts the combined pattern of Histograms of Oriented Gradients (HOG) and Local Binary pattern (LBP) for the training and detection. The motion information is utilized to adjust the confidence score of detect annotation, and the soft-NMS is used to handle the balance between the removing of redundant annotation and occlusion. The experiments indicate the proposed approach achieved a promising result compared to state-of-the-art trained with the benchmarking dataset.

关键词： Pedestrian Detection HOG LBP soft-NMS Mid-high Crowd Density

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