scholarly journals Hybrid Biometric Recognition using Stacked Auto Encoder with Random Forest Classifier

2020 ◽  
Vol 6 (2) ◽  
pp. 20-26
Author(s):  
Amreen Khan ◽  
Dr. Abhishek Bhatt
Keyword(s):  
Random Forest ◽  
High Performance ◽  
Research Work ◽  
Personal Data ◽  
Recognition System ◽  
Identification System ◽  
Optimum Approach ◽  
Vein Recognition ◽  
Effective Models ◽  
Result Analysis

In recent years, the need for security of personal data is becoming progressively important. A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. With respect to this concern, the identification system based on the fusion of multibiometric values is the most recommended in order to significantly improve and obtain high performance accuracy. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Retinex Algorithm, Stacked Deep Auto Encoder and Random forest (RF) classifier based on multi-biometric fingerprint as well as finger-vein recognition system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. In order to gain above mentioned objectives, fingerprint and fingervein dataset is taken for training and testing. The result analysis shows approx. 97% accuracy, 92% precision rate as well as 0.04 EER that shows enhancement over existing work.

scholarly journals Low Resolution Fingerprint Image Verification using CNN Filter and LSTM Classifier

2020 ◽  
Vol 8 (5) ◽  
pp. 3546-3549
Keyword(s):  
Short Term Memory ◽  
Research Work ◽  
Recognition System ◽  
Extraction Methods ◽  
Fingerprint Recognition ◽  
Biometric System ◽  
Fingerprint Verification ◽  
Optimum Approach ◽  
Reduced Rate ◽  
Result Analysis

A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. One of the main biometric system is fingerprint recognition system. The reduced rate of performance of fingerprint verification system is due to many reasons such as displacement of finger during scanning, moisture on scanner, etc. The result and accuracy of fingerprint recognition depends on the presence of valid minutiae. According to literature several Fingerprint Recognition System are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. This research work is focused on designing of fingerprint verification/classification including feature extraction methods and learning models for proper classification to label different fingerprints. In order to gain above mentioned objectives, FVC2002 dataset is taken for training and testing. In this dataset there are approx. 72 images which are used for testing purpose. In this dataset there are some blur, distorted as well as partial images also which are considered for recognition. Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) is used for recognition of fingerprint. The result analysis shows approx. 3% enhancement over existing work.

scholarly journals A Novel Approach Based Multi Biometric Finger Vein Template Recognition System using HGF

2021 ◽  
Vol 11 (1) ◽  
pp. 337-345
Author(s):  
Rahul Dev ◽  
Rohit Tripathi ◽  
Ruqaiya Khanam
Keyword(s):  
Research Work ◽  
Recognition System ◽  
Scale Invariant ◽  
Finger Vein ◽  
Novel Approach ◽  
Key Points ◽  
Vein Recognition ◽  
Scale Invariant Feature ◽  
Finger Vein Recognition ◽  
Recognition Technique

Abstract Finger vein(s) based biometrics is another way to deal with individual's distinguishing proof and has recently received much consideration. The strategy in light of low-level components, like the dark surface of finger vein is taken as standard. However, it is typically looked with numerous difficulties that involves affectability to noise and low neighbourhood consistency. Generally finger vein recognition in view of abnormal state highlights the portrayal that has ended up being a promising method to successfully defeat the above restrictions and enhance the framework execution. This research work proposes finger vein-based recognition technique making use of Hybrid BM3D Filter along with grouped sparse representation for image denoising and feature selection (Local Binary Pattern – LBP, Scale Invariant Feature Transform – SIFT) to evaluate features, key-points and perform recognition. The experimental results on two open databases of finger vein, i.e., HKPU and SDU show that the proposed method has enhanced the overall performance of finger vein pattern recognition system compared with other existing methods.

scholarly journals Automatic Vowel Recognition from Assamese Spoken words

2019 ◽  
Vol 8 (10) ◽  
pp. 2297-2304
Keyword(s):  
Random Forest ◽  
Speech Processing ◽  
Nearest Neighbor ◽  
Research Work ◽  
Recognition System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
North East ◽  
The North ◽  
Vowel Recognition

Vowel plays the most important role in any speech processing work. In this research work, recognition of Assamese vowel from spoken Assamese words is explored. Assamese is a language which is spoken by major people in Brahmaputra Valley of Assam, Assam is a state which is situated in the North-East part of India. This automatic vowel recognition system is implemented by using three efficient techniques Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) classifier. The database used in the experiments is specially designed for this purpose. A list of phonetically vowel rich Assamese words is prepared for the experiment. As an initial effort, twenty different (20) words uttered by fifty-five (55) speakers are taken. Utterances from both male and female speakers are collected. Each utterance was repeated two times by every speaker. A database of the total of 2200 samples is prepared. After experimenting on different samples it is seen that Random Forest (RF) is giving the best performance compared to the other two classifiers. The performance of the system is shown with testing dataset and comparison is done. Outcome of this research work will enhance the Machine Translation from Assamese to any other language.

scholarly journals Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images

2020 ◽  
Vol 6 ◽  
pp. e248 ◽  
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
High Performance ◽  
Personal Data ◽  
Region Of Interest ◽  
Identification System ◽  
Exposure Fusion ◽  
Finger Vein ◽  
Face Images ◽  
Image Contrast Enhancement ◽  
Personal Recognition ◽  
Efficient Matching

In recent years, the need for security of personal data is becoming progressively important. In this regard, the identification system based on fusion of multibiometric is most recommended for significantly improving and achieving the high performance accuracy. The main purpose of this paper is to propose a hybrid system of combining the effect of tree efficient models: Convolutional neural network (CNN), Softmax and Random forest (RF) classifier based on multi-biometric fingerprint, finger-vein and face identification system. In conventional fingerprint system, image pre-processed is applied to separate the foreground and background region based on K-means and DBSCAN algorithm. Furthermore, the features are extracted using CNNs and dropout approach, after that, the Softmax performs as a recognizer. In conventional fingervein system, the region of interest image contrast enhancement using exposure fusion framework is input into the CNNs model. Moreover, the RF classifier is proposed for classification. In conventional face system, the CNNs architecture and Softmax are required to generate face feature vectors and classify personal recognition. The score provided by these systems is combined for improving Human identification. The proposed algorithm is evaluated on publicly available SDUMLA-HMT real multimodal biometric database using a GPU based implementation. Experimental results on the datasets has shown significant capability for identification biometric system. The proposed work can offer an accurate and efficient matching compared with other system based on unimodal, bimodal, multimodal characteristics.

scholarly journals Impact of DanceSport on General Fitness from the Perspective of Chinese Athletes

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jiahui Xu ◽  
Xiaofen Li
Keyword(s):  
Energy Metabolism ◽  
Real Time ◽  
Performance Practice ◽  
High Performance ◽  
Research Work ◽  
Personal Data ◽  
Physiological Strain ◽  
Scientific Training ◽  
And Performance ◽  
Athlete Performance

Energy metabolism and motion are the essence of dance. Scientific training of athletes involves theoretical guidance in terms of fitness, talent-based selection, and high-performance practice. However, limited research work is carried out on the physiological strain of DanceSport competitions. Therefore, proper channel needs to be established for aerobic-based exercise on participant’s performance and general fitness. Competition simulation is used to collect personal data from real-time experimentations. Database gathers athlete information based on age, gender, and performance. Furthermore, results are obtained from experiment, record, and simulation in comparison to evaluate athlete performance. Main purpose of this article is to discover the characteristics of DanceSport from the perspectives of energetics in 32 domestic elite. Finally, World DanceSport Federation Judging System 2.1 “WFJS2.1” strategy is utilized for international game challenges.

IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Individual Identification ◽  
Work Place ◽  
Identification Process ◽  
Finger Vein ◽  
Noise Point ◽  
Vein Recognition ◽  
A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

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