Edge Detection with Sobel Operator

Oct 13, 2025 by sorusgentrification.com 10 views

Introduction

In today's digital era, facial recognition technology has become increasingly relevant for various applications, including security, authentication, and information technology. The accuracy and speed of facial recognition systems are crucial for their effectiveness. This study discusses the application of the Hybridization of the Learning Vector Quantization (LVQ) algorithm with self-organizing for face introduction using webcam. The goal is to improve the accuracy and speed of facial recognition systems.

Background

Facial recognition technology has been widely used in various fields, including security, authentication, and information technology. However, the accuracy and speed of facial recognition systems are still a challenge. The Learning Vector Quantization (LVQ) algorithm is a type of neural network that has been widely used for facial recognition. However, the LVQ algorithm has its limitations, including the need for a large amount of training data and the risk of overfitting. To overcome these limitations, this study proposes the hybridization of the LVQ algorithm with the Kadalah algorithm.

Methodology

The methodology of this study involves the following steps:

Data Collection: The data used in this study consists of facial digital images obtained through the camera. The images will be divided into two sets: a learning data set and a testing data set.
Preprocessing: The face images need to be processed first (preprocessing) to be ready to be used as input in the network. This preprocessing process is divided into four steps:

Reading Image: Take the face image of the existing source.
GrayScaling: Changing the color image into a grayscale image to simplify the data to be processed.
Edge Detection with Sobel Operator: Applying the edge detection method to accentuate the boundaries of the object in the image.

Hybridization: The Kadalah algorithm is used to determine the initial weight, which is then included in the LVQ algorithm. The final result of this process is a weight that is ready to be used to recognize the face.
Testing: The testing data set is used to evaluate the performance of the hybrid algorithm.

Results

The test results show that the percentage of successful face recognition for the LVQ algorithm reached 57.03%, Kohsien was 52.59%, and Hybrid recorded the highest number of 68.88%. In terms of recognition process time, the LVQ algorithm requires an average of 2.64 seconds, a 2.61 second, and hybrid is only 2.59 seconds.

Discussion

The results of this study show that the hybrid algorithm shows excellence in accuracy when compared to the Kohsen algorithm, which is 16.29%. In addition, in terms of the speed of the introduction process, the hybrid algorithm is more efficient than the LVQ and Kohsien with a significant time difference, which is 0.05 seconds faster than LVQ and 0.02 seconds than Kohsien.

Conclusion

The hybridization of the LVQ algorithm with the Kadalah algorithm is a promising approach for improving the accuracy and speed of facial recognition systems. The use of webcam as a data acquisition tool is a practical and efficient step, given the number of devices available today. This allows the system to be applied in real situations with uncomplicated use of devices. In the increasingly advanced digital era, face recognition technology becomes very relevant, both for security, authentication, and various other applications in the field of information technology. With the application of this hybrid algorithm, not only increases the accuracy and speed of facial recognition, but also opens the way for further research and development in this field.

Recommendations

Based on the results of this study, the following recommendations are made:

Further Research: Further research is needed to improve the accuracy and speed of facial recognition systems.
Application: The hybrid algorithm can be applied in various fields, including security, authentication, and information technology.
Development: The development of facial recognition technology is crucial for its effectiveness.

Limitations

This study has several limitations, including:

Data Quality: The quality of the data used in this study may affect the accuracy and speed of the facial recognition system.
Algorithm Complexity: The complexity of the algorithm used in this study may affect the accuracy and speed of the facial recognition system.
Hardware Requirements: The hardware requirements for the facial recognition system may affect its accuracy and speed.

Future Work

Future work includes:

Improving Accuracy: Improving the accuracy of the facial recognition system by using more advanced algorithms and techniques.
Increasing Speed: Increasing the speed of the facial recognition system by using more efficient algorithms and techniques.
Developing New Applications: Developing new applications for facial recognition technology, including security, authentication, and information technology.

Q: What is the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam?

A: The Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam is a type of facial recognition system that combines the Learning Vector Quantization (LVQ) algorithm with the Kadalah algorithm. This hybrid approach aims to improve the accuracy and speed of facial recognition systems.

Q: What are the benefits of using the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam?

A: The benefits of using the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam include:

Improved accuracy: The hybrid approach can improve the accuracy of facial recognition systems by up to 16.29% compared to the Kohsen algorithm.
Increased speed: The hybrid approach can also increase the speed of facial recognition systems by up to 0.05 seconds compared to the LVQ algorithm.
Practical and efficient: The use of webcam as a data acquisition tool is a practical and efficient step, given the number of devices available today.

Q: What are the limitations of the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam?

A: The limitations of the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam include:

Data quality: The quality of the data used in this study may affect the accuracy and speed of the facial recognition system.
Algorithm complexity: The complexity of the algorithm used in this study may affect the accuracy and speed of the facial recognition system.
Hardware requirements: The hardware requirements for the facial recognition system may affect its accuracy and speed.

Q: How does the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam work?

A: The Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam works by combining the Learning Vector Quantization (LVQ) algorithm with the Kadalah algorithm. The Kadalah algorithm is used to determine the initial weight, which is then included in the LVQ algorithm. The final result of this process is a weight that is ready to be used to recognize the face.

Q: What are the applications of the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam?

A: The applications of the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam include:

Security: The hybrid approach can be used in security applications, such as facial recognition for access control.
Authentication: The hybrid approach can be used in authentication applications, such as facial recognition for identity verification.
Information technology: The hybrid approach can be used in information technology applications, such as facial recognition for user identification.

Q: What are the future directions of the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam?

A: The future directions of the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam include:

Improving accuracy: Further research is needed to improve the accuracy of facial recognition systems.
Increasing speed: Further research is needed to increase the speed of facial recognition systems.
Developing new applications: New applications for facial recognition technology, including security, authentication, and information technology, are being developed.

Q: How can I get started with the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam?

A: To get started with the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam, you will need to:

Collect facial data: Collect a large dataset of facial images.
Preprocess the data: Preprocess the facial data to prepare it for use in the facial recognition system.
Train the model: Train the facial recognition model using the preprocessed data.
Test the model: Test the facial recognition model using a separate dataset.

Note: This is a general guide and may vary depending on the specific implementation of the Hybrid Algorithm Learning Vector Quantization (LVQ) on Facial Introduction Using Webcam.