Effect of Mean Filter on Face Image from Video Frames

Othman, Pawan Sh.; Marqas, Ridwan Boya; Abdulqader, Diler Naseradeen; Almufti, Saman M.

doi:10.1109/isdfs49300.2020.9116277

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…• Feature Importance Evaluation: A salient feature of Random Forest is its intrinsic ability to evaluate the importance of various features in the predictive process. This attribute is instrumental in interpretability and understanding the relative significance of different predictors in the model [45], [46].…”

Section: Amentioning

confidence: 99%

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Almufti,

Abdulazeez

2024

ijcs

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Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.

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Section: Amentioning

confidence: 99%

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Almufti,

Abdulazeez

2024

ijcs

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“…Similarly, 𝐼(𝑥 3 , 𝑦 3 ) = 31 + 43 + 21 − 20 = 75 A rectangular region is calculated using the integral image, for instance consider the following example where we have to find the rectangular feature Let A,B,C,D be the integral image values at the four corners of a rectangle feature. Then, within the rectangle feature, the total of original image values can be calculated as [33]:…”

Section: Integral Image Formationmentioning

confidence: 99%

A Secured Biometric Authentication with Hybrid Face Detection and Recognition Model

2023

IJIES

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Now-a-days biometrics is used to recognize an individual, and is thought of a more secure way to authorize an individual. Face is the most important factor to recognize an individual. In order to enforce security features, many verification systems use face recognition to identify a person, such as -automobile security, security of home, smartphones owner recognition, attendance system, identification of criminals, etc. Face biometric works in two phases -face detection and face recognition. In this paper we are proposing a hybrid model that works in two phases, face detection is the first phase which includes two stages, in first stage it detects the face from the acquired image and then in the second stage acquired image is subjected to intensity equalization. The second phase is of Face recognition, where finally the individual is recognized as authenticated user or not. We are using Viola-Jones algorithm to detect the face, histogram stretching for intensity equalization and principal component analysis (PCA) algorithm for image recognition. The effectiveness of the suggested facial recognition system is shown through experimental findings using the face dataset constituting of 100 subjects using VTU-BEC-DB multimodal biometrics database with constraints of pose and illumination in the face images, without the intermediate step of intensity equalisation, we had a recognition rate of 93.60%. However, when the intermediate step of intensity equalisation was included, a novelty was added to the hybrid algorithm, and we achieved a recognition rate of 98.20%, which is higher recognition rate than the state-of-the-art methods.

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Optimizing Image and Signal Processing Through the Application of Various Filtering Techniques: A Comparative Study

Gonzales,

Ramos,

Lacson

et al. 2023

Lecture Notes in Networks and Systems

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Effect of Mean Filter on Face Image from Video Frames

Cited by 4 publications

References 7 publications

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

A Secured Biometric Authentication with Hybrid Face Detection and Recognition Model

Optimizing Image and Signal Processing Through the Application of Various Filtering Techniques: A Comparative Study

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