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FRS-OCC: Face Recognition System for Surveillance Based on Occlusion Invariant Technique
Abbas, Qaisar International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.8
Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.
Lifesaver: Android-based Application for Human Emergency Falling State Recognition
Abbas, Qaisar International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.8
Smart application is developed in this paper by using an android-based platform to automatically determine the human emergency state (Lifesaver) by using different technology sensors of the mobile. In practice, this Lifesaver has many applications, and it can be easily combined with other applications as well to determine the emergency of humans. For example, if an old human falls due to some medical reasons, then this application is automatically determining the human state and then calls a person from this emergency contact list. Moreover, if the car accidentally crashes due to an accident, then the Lifesaver application is also helping to call a person who is on the emergency contact list to save human life. Therefore, the main objective of this project is to develop an application that can save human life. As a result, the proposed Lifesaver application is utilized to assist the person to get immediate attention in case of absence of help in four different situations. To develop the Lifesaver system, the GPS is also integrated to get the exact location of a human in case of emergency. Moreover, the emergency list of friends and authorities is also maintained to develop this application. To test and evaluate the Lifesaver system, the 50 different human data are collected with different age groups in the range of (40-70) and the performance of the Lifesaver application is also evaluated and compared with other state-of-the-art applications. On average, the Lifesaver system is achieved 95.5% detection accuracy and the value of 91.5 based on emergency index metric, which is outperformed compared to other applications in this domain.
Evaluation on the Performance of Deep Excavation by Using PIV Technique
Qaisar Abbas,송주상,유충식 한국지반신소재학회 2017 한국지반신소재학회 논문집 Vol.16 No.4
The concern study, present the results of experimental study on the performance of deep excavation by using image processing technique particle image velocimetry (PIV). The purpose of present study is to check the application of PIV for the successive ground deformation during deep excavation. To meet the objectives of concern study, a series of reduce scale model test box experiments are performed by considering the wall stiffness, ground water table effect and ground relative density. The results are presented in form of contour and vector plots and further based on PIV analysis wall and ground displacement profile are drawn. The results of present study, indicate that, the PIV technique is useful to demonstrate the ground deformation zone during the successive ground excavation as the degree of accuracy in PIV analysis and measured results with LVDT are within 1%. Further the vector and contours plot effectively demonstrate the ground behavior under different conditions and the PIV analysis results fully support the measured results.
Evaluation on the Performance of Deep Excavation by Using PIV Technique
Abbas, Qaisar,Song, Ju-sang,Yoo, Chung-Sik Korean Geosynthetics Society 2017 한국지반신소재학회 논문집 Vol.16 No.4
The concern study, present the results of experimental study on the performance of deep excavation by using image processing technique particle image velocimetry (PIV). The purpose of present study is to check the application of PIV for the successive ground deformation during deep excavation. To meet the objectives of concern study, a series of reduce scale model test box experiments are performed by considering the wall stiffness, ground water table effect and ground relative density. The results are presented in form of contour and vector plots and further based on PIV analysis wall and ground displacement profile are drawn. The results of present study, indicate that, the PIV technique is useful to demonstrate the ground deformation zone during the successive ground excavation as the degree of accuracy in PIV analysis and measured results with LVDT are within 1%. Further the vector and contours plot effectively demonstrate the ground behavior under different conditions and the PIV analysis results fully support the measured results.
유충식,양재원,Qaisar Abbas,HAIDER SYED AIZAZ 한국지반신소재학회 2018 한국지반신소재학회 논문집 Vol.17 No.4
This research concerns the prediction method for ground movement and wall member force due to determination structural stability check and failure check during deep excavation construction. First, research related with excavation influence parameters is conducted. Then, numerical analysis for various excavation conditions were conducted using Finite Element Method and Beam-column elasto-plasticity method. Excavation analysis database was then constructed. Using this database, development of ANN (artificial neural network) was performed for each ground movements and using structural member forces. By comparing the numerical analysis results with ANN’s prediction, it is validated that development of ANN can be used efficient for prediction of ground movement and structural member forces in deep excavation site. 본 연구에서는 깊은 굴착에 따른 인접구조물의 손상 평가 및 벽체 구조물의 안정성 평가를 하기 위한 지표의 거동 및 벽체부재력의 효율적인 예측기법에 대한 내용을 다루었다. 우선적으로 지표의 거동 및 벽체 부재력에 영향을 미치는 매개 변수에대한 연구를 수행하였고, 이를 토대로 다양한 굴착 조건에 대해 수치해석을 실시한 결과를 통해 데이터베이스를 구축하였다. 구축된 데이터베이스를 토대로 벽체의 부재력과 지표의 거동 각각의 해석 결과에 대한 인공신경망 엔진 학습을 수행하였으며학습된 인공신경망을 이용하여 예측된 결과와 사용된 데이터베이스의 결과를 비교하여 인공신경망 엔진이 벽체의 부재력및 지표의 거동예측에 효율적임을 검증하였다.