scholarly journals Scalable Skin Lesion Multi-classification Recognition System

2020 ◽  
Vol 62 (2) ◽  
pp. 801-816
Author(s):  
Fan Liu ◽  
Jianwei Yan ◽  
Wantao Wang ◽  
Jian Liu ◽  
Junying Li ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Recognition System ◽  
Multi Classification ◽  
Classification Recognition

scholarly journals Human activity recognition via smart-belt in wireless body area networks

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771984935 ◽  
Author(s):  
Yuhong Zhu ◽  
Jingchao Yu ◽  
Fengye Hu ◽  
Zhijun Li ◽  
Zhuang Ling
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Recognition System ◽  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Human Activity Recognition ◽  
Support Vector ◽  
Body Area ◽  
Data Collector ◽  
Multi Classification

Human activity recognition based on wireless body area networks plays an essential role in various applications such as health monitoring, rehabilitation, and physical training. Currently, most of the human activity recognition is based on smartphone, and it provides more possibilities for this task with the rapid proliferation of wearable devices. To obtain satisfactory accuracy and adapt to various scenarios, we built a smart-belt which embedded the VG350 as posture data collector. This article proposes a hierarchical activity recognition structure, which divides the recognition process into two levels. Then a multi-classification Support Vector Machine algorithm optimized by Particle Swarm Optimization is applied to identify five kinds of conventional human postures. And we compare the effectiveness of triaxial accelerometer and gyroscope when used together and separately. Finally, we conduct systematic performance analysis. Experimental results show that our overall classification accuracy is 92.3% and the F-Measure can reach 92.63%, which indicates the human activity recognition system is accurate and effective.

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    

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