Analyzing Of Different Features Using Haar Cascade Classifier

Yustiawati, Ratna; Husni, Nyayu Latifah; Evelina, Evelina; Rasyad, Sabilal; Lutfi, Iskandar; Silvia, Ade; Alfarizal, Niksen; Rialita, Adella

doi:10.1109/icecos.2018.8605266

Cited by 18 publications

(5 citation statements)

References 4 publications

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“…The resulting .xml file generated by Cascade Trainer is read by MATLAB to detect objects, subsequently outlined with rectangles and labeled. This approach enhances the accuracy of object detection and labeling, addressing limitations in prior methods while maintaining minimal incorrect refusals [21].…”

Section: Background Of the Studymentioning

confidence: 99%

Precision Face Mask Detection in Crowded Environment using Machine Vision

Alsayaydeh,

Yusof,

Lin

et al. 2024

IJACSA

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In the face of rampant global disease transmission, effective preventive strategies are imperative. This study tackles the challenge of ensuring compliance in crowded settings by developing a sophisticated face mask detection system. Utilizing MATLAB and the Cascade Object detector, the system focuses on detecting white surgical masks in frontal images. Training the system is critical for accuracy; therefore, cross-validation is employed due to limited data. The results reveal accuracies of 76.67% for initial training, 67.50% for a 9:11 cropping ratio, and 89.17% for a 9:4:7 cropping ratio, highlighting the system's remarkable precision in mask detection. Looking ahead, the system's adaptability can be further expanded to include various mask colors and types, extending its effectiveness beyond COVID-19 to combat a range of respiratory illnesses. This research represents a significant advancement in reinforcing preventive measures against future disease outbreaks, especially in densely populated environments, contributing significantly to global public health and safety initiatives.

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Section: Background Of the Studymentioning

confidence: 99%

Precision Face Mask Detection in Crowded Environment using Machine Vision

Alsayaydeh,

Yusof,

Lin

et al. 2024

IJACSA

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“…The main goal of feature extraction is to obtain the most relevant information from the original data and represent that information in a reduced dimensional space [36]. The proposed diagnostic system extracted 68 facial features using Haar Cascades, which is an popular technique used to quickly detect objects, including human faces [37]. Facial features were extracted in the proposed diagnostic system using rectangles to cover each facial feature.…”

Section: Features Extractionmentioning

confidence: 99%

Automatic Facial Palsy, Age and Gender Detection Using a Raspberry Pi

et al. 2023

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Facial palsy (FP) is a neurological disorder that affects the facial nerve, specifically the seventh nerve, resulting in the patient losing control of the facial muscles on one side of the face. It is an annoying condition that can occur in both children and adults, regardless of gender. Diagnosis by visual examination, based on differences in the sides of the face, can be prone to errors and inaccuracies. The detection of FP using artificial intelligence through computer vision systems has become increasingly important. Deep learning is the best solution for detecting FP in real-time with high accuracy, saving patients time, effort, and cost. Therefore, this work proposes a real-time detection system for FP, and for determining the patient’s gender and age, using a Raspberry Pi device with a digital camera and a deep learning algorithm. The solution facilitates the diagnosis process for both the doctor and the patient, and it could be part of a medical assessment activity. This study used a dataset of 20,600 images, containing 19,000 normal images and 1600 FP images, to achieve an accuracy of 98%. Thus, the proposed system is a highly accurate and capable medical diagnostic tool for detecting FP.

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“…The Haar cascade classifier, which is successful in detecting small objects, has been used in face recognition applications for many years. Haar, which is relatively slower than other recognition models, uses a multi-stage control structure for object recognition [20,21]. Within the scope of the study, the Haar cascade object recognition model was preferred in the system described here because of its success in detecting small objects in large areas.…”

Section: Object Recognition Model Determinationmentioning

confidence: 99%

Real-time Fast Selection System with Object Recognition and TSP algorithms

DEMİR

2023

International Journal of Applied Mathematics Electronics and Computers

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The stage before the conversion of agricultural products into post-harvest consumer products is the process of separating the raw products into appropriate classes. Today, this difficult manual separating process is a process in which a large number of workers work at an intense pace on the product line and the workforce is intensively spent. Disruptions in separating as a result of carelessness cause product loss, loss of time and cost increases. In this study, as an alternative to manual separating processes, a real-time separating system, which detects the products in the factory band with object recognition methods and enables fast positioning of the separating tool on the products, works simultaneously with object recognition and traveling salesman problem algorithms has been created. In this way, a low-budget separating system is recommended for large selecting processes with a time- and cost-effective selecting model. In the study, the creation of a real-time fast separating system with the support of the traveling salesman algorithm, performance evaluation and research and findings on the fast separating model are presented.

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Analyzing Of Different Features Using Haar Cascade Classifier

Cited by 18 publications

References 4 publications

Precision Face Mask Detection in Crowded Environment using Machine Vision

Precision Face Mask Detection in Crowded Environment using Machine Vision

Automatic Facial Palsy, Age and Gender Detection Using a Raspberry Pi

Real-time Fast Selection System with Object Recognition and TSP algorithms

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